From 57d79697e4886345b85d2d5539e6df193a001367 Mon Sep 17 00:00:00 2001
From: MIlhamR <2110511078@mahasiswa.upnvj.ac.id>
Date: Fri, 11 Jul 2025 09:54:00 +0000
Subject: [PATCH] Upload files to "/"

---
 Data Scrapping.ipynb                         |  160 +
 EDA.ipynb                                    | 1876 +++++++++
 GRU.ipynb                                    | 3866 ++++++++++++++++++
 Graph.ipynb                                  | 2599 ++++++++++++
 LSTM.ipynb                                   | 3611 ++++++++++++++++
 PT Adaro Energy Indonesia Tbk_2020-2024.csv  | 1149 ++++++
 PT Dian Swastatika Sentosa Tbk_2020-2024.csv | 1149 ++++++
 7 files changed, 14410 insertions(+)
 create mode 100644 Data Scrapping.ipynb
 create mode 100644 EDA.ipynb
 create mode 100644 GRU.ipynb
 create mode 100644 Graph.ipynb
 create mode 100644 LSTM.ipynb
 create mode 100644 PT Adaro Energy Indonesia Tbk_2020-2024.csv
 create mode 100644 PT Dian Swastatika Sentosa Tbk_2020-2024.csv

diff --git a/Data Scrapping.ipynb b/Data Scrapping.ipynb
new file mode 100644
index 0000000..8394e78
--- /dev/null
+++ b/Data Scrapping.ipynb	
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7075dca8-7af9-4d23-af98-673deed9a15d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install yfinance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0cd03d32-0109-444e-8acf-71b9d274c664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yfinance as yf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b93e219d-d30a-409b-abb8-557506ee897d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open    High     Low   Close    Adj Close     Volume\n",
+      "Date                                                              \n",
+      "2020-01-02  1555.0  1555.0  1490.0  1495.0   835.632202   76612000\n",
+      "2020-01-03  1460.0  1470.0  1425.0  1465.0   856.372375  117795600\n",
+      "2020-01-06  1455.0  1515.0  1450.0  1465.0   856.372375   61423200\n",
+      "2020-01-07  1480.0  1540.0  1460.0  1540.0   900.213867   74336500\n",
+      "2020-01-08  1530.0  1535.0  1505.0  1505.0   879.754395   55121100\n",
+      "...            ...     ...     ...     ...          ...        ...\n",
+      "2024-09-23  3720.0  3740.0  3690.0  3700.0  3700.000000   70067100\n",
+      "2024-09-24  3730.0  3760.0  3690.0  3740.0  3740.000000  111192200\n",
+      "2024-09-25  3760.0  3760.0  3680.0  3690.0  3690.000000   75405400\n",
+      "2024-09-26  3720.0  3780.0  3700.0  3780.0  3780.000000   76548100\n",
+      "2024-09-27  3780.0  3940.0  3740.0  3910.0  3910.000000  121060900\n",
+      "\n",
+      "[1148 rows x 6 columns]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Mendownload data saham\n",
+    "data = yf.download(\"ADRO.JK\", start=\"2020-01-01\", end=\"2024-09-30\")\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5166ea97-1a80-430c-9d63-aae43503f363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.to_csv('PT Adaro Energy Indonesia Tbk_2020-2024.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d48de37b-0ff9-459c-a214-c7efffeed321",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Open     High      Low    Close  Adj Close   Volume\n",
+      "Date                                                              \n",
+      "2020-01-02   1360.0   1435.0   1360.0   1435.0     1435.0     5000\n",
+      "2020-01-03   1435.0   1435.0   1435.0   1435.0     1435.0        0\n",
+      "2020-01-06   1437.5   1437.5   1435.0   1435.0     1435.0     3000\n",
+      "2020-01-07   1435.0   1435.0   1435.0   1435.0     1435.0        0\n",
+      "2020-01-08   1435.0   1435.0   1435.0   1435.0     1435.0        0\n",
+      "...             ...      ...      ...      ...        ...      ...\n",
+      "2024-09-23  41300.0  41575.0  41000.0  41500.0    41500.0  2201800\n",
+      "2024-09-24  41550.0  42150.0  41025.0  41075.0    41075.0  2311300\n",
+      "2024-09-25  41075.0  41250.0  40250.0  41050.0    41050.0  2263200\n",
+      "2024-09-26  41000.0  42000.0  40925.0  41550.0    41550.0  2214700\n",
+      "2024-09-27  41675.0  42000.0  41050.0  41525.0    41525.0  2261300\n",
+      "\n",
+      "[1148 rows x 6 columns]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "data2 = yf.download(\"DSSA.JK\", start=\"2020-01-01\", end=\"2024-09-30\")\n",
+    "print(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "44b196c7-a29d-4ee5-aaea-405f1e0d8dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2.to_csv('PT Dian Swastatika Sentosa Tbk_2020-2024.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/EDA.ipynb b/EDA.ipynb
new file mode 100644
index 0000000..5bc3485
--- /dev/null
+++ b/EDA.ipynb
@@ -0,0 +1,1876 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import Libraries"
+      ],
+      "metadata": {
+        "id": "XTBQCgfi4s_3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "13IWG7jegtHo"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import missingno as msno\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import Function"
+      ],
+      "metadata": {
+        "id": "jBJgBJXC49as"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_stock_data(df):\n",
+        "    plt.figure(figsize=(16, 5))\n",
+        "\n",
+        "    plt.plot(df['Date'].values, df.Open.values, color='orange', label='Open')\n",
+        "    plt.plot(df['Date'].values, df.Close.values, color='green', label='Close')\n",
+        "    plt.plot(df['Date'].values, df[\"Adj Close\"].values, color='black', label='Adj Close')\n",
+        "    plt.plot(df['Date'].values, df.Low.values, color='blue', label='Low')\n",
+        "    plt.plot(df['Date'].values, df.High.values, color='red', label='High')\n",
+        "    plt.xticks(np.arange(20,df.shape[0],50), rotation=20)\n",
+        "    plt.xlabel('Date')\n",
+        "    plt.ylabel('Price')\n",
+        "    plt.legend(loc='best')\n",
+        "    plt.grid(True)\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "cfXJntJcjwSG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_correlation_heatmap(df):\n",
+        "  df = df.drop(columns=[\"Date\"])\n",
+        "  plt.figure(figsize=(8,6))\n",
+        "  sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\", linewidths=0.5, annot_kws={\"size\": 12})\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "MLBGr_IvnX80"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_histogram(df):\n",
+        "    plt.figure(figsize=(16, 12))\n",
+        "\n",
+        "    plt.subplot(3, 2, 1)\n",
+        "    sns.histplot(df['Open'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['Open'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.subplot(3, 2, 2)\n",
+        "    sns.histplot(df['Close'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['Close'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.subplot(3, 2, 3)\n",
+        "    sns.histplot(df['High'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['High'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.subplot(3, 2, 4)\n",
+        "    sns.histplot(df['Low'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['Low'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.subplot(3, 2, 5)\n",
+        "    sns.histplot(df['Adj Close'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['Adj Close'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.subplot(3, 2, 6)\n",
+        "    sns.histplot(df['Volume'], bins=30, color='blue', fill=False, stat='density')\n",
+        "    sns.kdeplot(df['Volume'], color='red')\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "vK7UB1Z5pCW8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Reading Dataset"
+      ],
+      "metadata": {
+        "id": "_XF8Df9v4v3C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ADRO"
+      ],
+      "metadata": {
+        "id": "f30_aaa44yhE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_adro = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Adaro%20Energy%20Indonesia%20Tbk_2020-2024.csv\"\n",
+        "adro = pd.read_csv(url_adro)\n",
+        "adro.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "CJ69vEFiDxO3",
+        "outputId": "cf4f10ba-a57f-4fa7-ad84-d5fbc2d1867b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close   Adj Close     Volume\n",
+              "0  2020-01-02  1555.0  1555.0  1490.0  1495.0  835.632202   76612000\n",
+              "1  2020-01-03  1460.0  1470.0  1425.0  1465.0  856.372375  117795600\n",
+              "2  2020-01-06  1455.0  1515.0  1450.0  1465.0  856.372375   61423200\n",
+              "3  2020-01-07  1480.0  1540.0  1460.0  1540.0  900.213867   74336500\n",
+              "4  2020-01-08  1530.0  1535.0  1505.0  1505.0  879.754395   55121100"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c25d4414-0f8a-42c4-99f8-394cf3a879c0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1490.0</td>\n",
+              "      <td>1495.0</td>\n",
+              "      <td>835.632202</td>\n",
+              "      <td>76612000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1470.0</td>\n",
+              "      <td>1425.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>117795600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1455.0</td>\n",
+              "      <td>1515.0</td>\n",
+              "      <td>1450.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>61423200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1480.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>900.213867</td>\n",
+              "      <td>74336500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1530.0</td>\n",
+              "      <td>1535.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>879.754395</td>\n",
+              "      <td>55121100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c25d4414-0f8a-42c4-99f8-394cf3a879c0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c25d4414-0f8a-42c4-99f8-394cf3a879c0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c25d4414-0f8a-42c4-99f8-394cf3a879c0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-fd25f41a-abb9-47c6-8901-82778e61c176\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd25f41a-abb9-47c6-8901-82778e61c176')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-fd25f41a-abb9-47c6-8901-82778e61c176 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro",
+              "summary": "{\n  \"name\": \"adro\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 913.246493337149,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          3120.0,\n          1670.0,\n          3090.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 924.8420313199422,\n        \"min\": 670.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          3020.0,\n          1930.0,\n          3320.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 902.619092508634,\n        \"min\": 610.0,\n        \"max\": 4080.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          2870.0,\n          1900.0,\n          3120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 914.3952655818309,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          3290.0,\n          775.0,\n          3640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 865.5150705162999,\n        \"min\": 377.03759765625,\n        \"max\": 3910.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          2913.447509765625,\n          2624.987548828125,\n          776.3223266601562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68712203,\n        \"min\": 0,\n        \"max\": 539995200,\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          57485800,\n          121344700,\n          40850500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B7p8nU8okoXL",
+        "outputId": "de4a8a6b-1521-4538-feee-bb56477ce10d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "CFiwlYrNk5dy",
+        "outputId": "430bf9e5-f8fb-43eb-b1c2-1655a1069078"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              Open         High          Low        Close    Adj Close  \\\n",
+              "count  1148.000000  1148.000000  1148.000000  1148.000000  1148.000000   \n",
+              "mean   2239.446864  2276.310976  2201.424216  2236.968641  1712.117432   \n",
+              "std     913.246493   924.842031   902.619093   914.395266   865.515071   \n",
+              "min     645.000000   670.000000   610.000000   645.000000   377.037598   \n",
+              "25%    1270.000000  1295.000000  1240.000000  1260.000000   782.738098   \n",
+              "50%    2400.000000  2425.000000  2370.000000  2400.000000  1984.760681   \n",
+              "75%    2910.000000  2950.000000  2870.000000  2910.000000  2353.506042   \n",
+              "max    4140.000000  4250.000000  4080.000000  4140.000000  3910.000000   \n",
+              "\n",
+              "             Volume  \n",
+              "count  1.148000e+03  \n",
+              "mean   8.789910e+07  \n",
+              "std    6.871220e+07  \n",
+              "min    0.000000e+00  \n",
+              "25%    4.269002e+07  \n",
+              "50%    6.984515e+07  \n",
+              "75%    1.077192e+08  \n",
+              "max    5.399952e+08  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-900c2cfa-370d-40c1-b672-ee815b92c91e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1.148000e+03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2239.446864</td>\n",
+              "      <td>2276.310976</td>\n",
+              "      <td>2201.424216</td>\n",
+              "      <td>2236.968641</td>\n",
+              "      <td>1712.117432</td>\n",
+              "      <td>8.789910e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>913.246493</td>\n",
+              "      <td>924.842031</td>\n",
+              "      <td>902.619093</td>\n",
+              "      <td>914.395266</td>\n",
+              "      <td>865.515071</td>\n",
+              "      <td>6.871220e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>645.000000</td>\n",
+              "      <td>670.000000</td>\n",
+              "      <td>610.000000</td>\n",
+              "      <td>645.000000</td>\n",
+              "      <td>377.037598</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1270.000000</td>\n",
+              "      <td>1295.000000</td>\n",
+              "      <td>1240.000000</td>\n",
+              "      <td>1260.000000</td>\n",
+              "      <td>782.738098</td>\n",
+              "      <td>4.269002e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>2400.000000</td>\n",
+              "      <td>2425.000000</td>\n",
+              "      <td>2370.000000</td>\n",
+              "      <td>2400.000000</td>\n",
+              "      <td>1984.760681</td>\n",
+              "      <td>6.984515e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2910.000000</td>\n",
+              "      <td>2950.000000</td>\n",
+              "      <td>2870.000000</td>\n",
+              "      <td>2910.000000</td>\n",
+              "      <td>2353.506042</td>\n",
+              "      <td>1.077192e+08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4140.000000</td>\n",
+              "      <td>4250.000000</td>\n",
+              "      <td>4080.000000</td>\n",
+              "      <td>4140.000000</td>\n",
+              "      <td>3910.000000</td>\n",
+              "      <td>5.399952e+08</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-900c2cfa-370d-40c1-b672-ee815b92c91e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-900c2cfa-370d-40c1-b672-ee815b92c91e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-900c2cfa-370d-40c1-b672-ee815b92c91e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d89ca680-8854-467f-a7d0-22d64544be96\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d89ca680-8854-467f-a7d0-22d64544be96')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d89ca680-8854-467f-a7d0-22d64544be96 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"adro\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1188.4519864434749,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2239.446864111498,\n          2400.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1217.098635414093,\n        \"min\": 670.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2276.310975609756,\n          2425.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1174.6650786365867,\n        \"min\": 610.0,\n        \"max\": 4080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2201.4242160278745,\n          2370.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1189.056512993077,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2236.968641114983,\n          2400.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1131.2377072789996,\n        \"min\": 377.03759765625,\n        \"max\": 3910.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1712.1174319862073,\n          1984.7606811523438,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176176513.10891184,\n        \"min\": 0.0,\n        \"max\": 539995200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          87899098.7804878,\n          69845150.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_stock_data(adro)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 438
+        },
+        "id": "DlW_VXsjnCxT",
+        "outputId": "25cd29ac-51c3-4063-ec8f-2f72c8097848"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_histogram(adro)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 899
+        },
+        "id": "OtNiqxqdqFov",
+        "outputId": "cd3d1e49-a6af-4895-e6e5-77119fd59378"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x1200 with 6 Axes>"
+            ],
+            "image/png": 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nTh1VrlzZ1fEBAAAAgMvQl0GBOXlS2rnT7HvyDA3JLDv15ZfShx9KY8dK3t52RwQAAIqJPCU0MlWrVk3VqlVzVSwACsJff0krV0phYVL9+pLDYXdEAAAAtqMvA5fbtk2yLKlsWTOLwZPdfrsUEiLt2yctWSI1b253RAAAoJjIU0IjPT1dsbGxWrhwoQ4dOqSMjIxsjy9atMglwQHIm3LWYQ3JGCfV/V7atCnrgbAwqWVL6YEHpBtvtC0+AAAAu9CXQYE5uyC4pw8iCgiQOnaU3nzTLDtFQgMAABSSPCU0+vfvr9jYWLVu3Vq1a9eWw9Mv1oCiZOVK/Zx2ty7XXikzl1G1qnTggJSQIL33nhQbK40ZIz35pOTlZWe0AAAAhYq+DApMcSgIfrb77zcJjU8+kaZMkVTS7ogAAEAxkKeExqxZs/Txxx+rVatWro4HQF5ZlvT221K/frpcZ7Rd1VTt47HSTTdJ5ctLp09LP/4ovf++GUU1apS0dq35unRpu6MHAAAoFPRlUGAyZ2h4ckHwszVpIl1xhfTHH9Lnn0u6z+6IAABAMZCnodl+fn666qqrXB0LgPwYN056+GHpzBl94WinJj4rpQ4dTDJDkvz9pehok8B4913Jz0+aO9d0RBIT7YwcAACg0NCXQYEpbjM0HA6pa1ez/8EH9sYCAACKjTwlNAYNGqRXXnlFlmW5Oh4AefH999KIEWb/mWfUwfszJTuCz9/+wQfNbI0KFaTNm01H5F/rRwMAAHgi+jIoEKmp0o4dZr+4zNCQpPv+mZXx/fcKTdtvbywAAKBYyNOSUz/99JMWL16sb775RldffbV8fX2zPf7ZZ5+5JDgAF2HfPqlzZ7PkVI8eJrHx9EUc17ixNG+emaHx1VdmhseTTxZ4uAAAAHaiL4MC8dtvUlqaVKqUdNlldkdTeK68Urr+emnZMrU5NlPvXPK43REBAAAPl6eERkhIiO68805XxwIgt1JTpXvukY4ckerXl159NXfHX3ON9PrrJhEycqR07bXSbbcVSKgAAADugL4MCkTmclM1apilmIqT+++Xli3THcdmkNAAAAAFLk8Jjffee8/VcQDIi9Gjpfh4KThY+uQTKTAw98/x4IPSzz+bguKdO0ubNkkKd3moAAAA7oC+DArE1q3mtrjUzzhbhw5Sv36qeWaDqp7eLKm23REBAAAPlqcaGpKUlpam77//Xm+++aaOHTsmSTpw4ICOHz+eq+eZMmWKqlSpooCAAEVGRmrlypUXbD9nzhzVqFFDAQEBqlOnjubPn5/tccuyNGrUKFWoUEGBgYGKjo7Wjsy1TP9x9OhRdenSRUFBQQoJCVGPHj3OiduyLL344ouqVq2a/P39VbFiRT377LO5em1AgfrjD2niRLP/7rtmundeTZ5sZmscPSo98YRr4gMAAHBTrurLAE7btpnb6tXtjcMOZcpILVtKku44NsPmYAAAgKfLU0Jj9+7dqlOnjtq2bas+ffro8OHDkqTx48dr8ODBF/08s2fP1sCBAzV69GitXbtW9erVU0xMjA4dOpRj++XLl6tz587q0aOH1q1bp3bt2qldu3bavHmzs82ECRM0efJkTZ06VStWrFDJkiUVExOjU6dOOdt06dJFv/zyixYsWKB58+bphx9+UK9evbKdq3///nrnnXf04osvauvWrfryyy/VuHHj3HybgII1bJh05owUHS3ddVf+nisgQJo61UyP//BDNTi53DUxAgAAuBlX9WWAbIpzQkOSunSRJLU5PlPKyLA5GAAA4MkclmVZuT2oXbt2Kl26tN59912VLVtWGzZs0BVXXKElS5aoZ8+e58yIOJ/IyEhde+21eu211yRJGRkZqlSpkvr166ehQ4ee075jx45KSUnRvHnznPddd911ql+/vqZOnSrLshQeHq5BgwY5OyNJSUkKDQ1VbGysOnXqpC1btqhWrVpatWqVGjVqJEmKi4tTq1attG/fPoWHh2vLli2qW7euNm/erOr5uCBNTk5WcHCwkpKSFBQUlOfnQfFUtaq5zfHXKT7eFPN2OKR166R69bI97Odnbs+cyeVJe/SQpk3TJv+Gan/ZSm3/Lc+TuIACc8HfjYuQ598PF5wbANxZcbl2dVVfxg7F5T1yd+dcD1iWmaWQmCht2CDVrXtRz5OfaxJXPodLnufkSR0vGapS1jHphx+kG27IX0AAAMAjFMT1a54+rfzxxx81YsQI+WVe9fyjSpUq2r9//0U9x5kzZ7RmzRpFR0dnBePlpejoaMXHx+d4THx8fLb2khQTE+Nsv3PnTiUkJGRrExwcrMjISGeb+Ph4hYSEOJMZkhQdHS0vLy+tWLFCkvTVV1/piiuu0Lx58xQREaEqVarooYce0tGjRy/qtQEFyrKkgQPNfvfu5yQz8uW556SgINU5vUbtj7G+NAAA8Dyu6MsA2Rw5YpIZDkdWtqO4CQzUd6X+mTU+g2WnAABAwclTQiMjI0Pp6enn3L9v3z6VLl36op7jyJEjSk9PV2hoaLb7Q0NDlZCQkOMxCQkJF2yfeftfbcqXL5/tcR8fH5UpU8bZ5o8//tDu3bs1Z84cvf/++4qNjdWaNWt09913X/A1nT59WsnJydk2wOXmzDFFvEuUkJ55xrXPHRpqCo1LGvzXMNMxAwAA8CCu6Mtkym09wMTERPXp00cVKlSQv7+/qlWrdk5NQBRBmctNXX65FBhobyw2+rK0WXZKc+bkf8oIAADAeeQpoXHbbbfp5Zdfdn7tcDh0/PhxjR49Wq1atXJVbLbJyMjQ6dOn9f777+uGG25Qs2bN9O6772rx4sXalnmxmoNx48YpODjYuVWqVKkQo0axkJEhjRxp9ocMkcLDXX+Ovn31u28NlU0/LE2a5PrnBwAAsJGr+jK5rQd45swZ3Xrrrdq1a5c++eQTbdu2TW+//bYqVqyY35cEuxX3+hn/iA+8RYe8w6SjR6W4OLvDAQAAHipPCY2JEydq2bJlqlWrlk6dOqV7773XOUV7/PjxF/Uc5cqVk7e3tw4ePJjt/oMHDyosLCzHY8LCwi7YPvP2v9r8u5ORlpamo0ePOttUqFBBPj4+qlatmrNNzZo1JUl79uw572saNmyYkpKSnNvevXvP2xbIk3nzpO3bpZAQadCggjmHn59eLvvPzI9XX5WYaQQAADyIK/oykjRp0iT17NlT3bt3V61atTR16lSVKFFC06ZNy7H9tGnTdPToUc2dO1fXX3+9qlSpoptuukn1XLl8KOyxfbu5Pav/WBxlOLw1r1Rn8wXLTgEAgAKSp4TGZZddpg0bNmj48OF67LHH1KBBAz3//PNat27dOcs5nY+fn58aNmyohQsXOu/LyMjQwoULFRUVleMxUVFR2dpL0oIFC5ztIyIiFBYWlq1NcnKyVqxY4WwTFRWlxMRErVmzxtlm0aJFysjIUGRkpCTp+uuvV1pamn7//Xdnm+3/XKRWrlz5vK/J399fQUFB2TbApTJnTDz8sFSqVIGd5tuSd+l33xpmyak33iiw8wAAABQ2V/Rl8lIP8Msvv1RUVJT69Omj0NBQ1a5dW88991yOy19lYknbIoIZGk7OZae+/JKBUQAAoED45PlAHx/dd999+Tr5wIED1a1bNzVq1EiNGzfWyy+/rJSUFHXv3l2S1LVrV1WsWFHjxo2TJPXv31833XSTJk6cqNatW2vWrFlavXq13nrrLUlmuviAAQM0duxYVa1aVRERERo5cqTCw8PVrl07SWamRYsWLdSzZ09NnTpVqamp6tu3rzp16qTwf5bviY6O1jXXXKMHH3xQL7/8sjIyMtSnTx/deuut2WZtAIVqzRpp6VLJx0fq27dAT2U5vDT1kmF64VA3k0T53/+K9XrAAADAs+S3L3OheoBbt27N8Zg//vhDixYtUpcuXTR//nz99ttvevTRR5WamqrR/9Qw+7dx48ZpzJgxeY4ThYSEhtMv/teY78O2bdJnn0kPPGB3SAAAwMPkKaHx/vvvX/Dxrl27XtTzdOzYUYcPH9aoUaOUkJCg+vXrKy4uztkx2LNnj7y8siaRNGnSRDNnztSIESM0fPhwVa1aVXPnzlXt2rWdbYYMGaKUlBT16tVLiYmJatq0qeLi4hQQEOBsM2PGDPXt21fNmzeXl5eX2rdvr8mTJzsf9/Ly0ldffaV+/frpxhtvVMmSJdWyZUtNnDjxol4XUCAyZ2d06iRddlmBn25e6c56ocRoadcu6Z13pH79CvycAAAABc1VfZncysjIUPny5fXWW2/J29tbDRs21P79+/XCCy+cN6ExbNgwDRw40Pl1cnIydfr+396dx9lY/n8cf53ZzYwxZJdsWbOTJVSYoqjIr6yFxDeliFIqkhZSSkpZylKRpUUiJGuL7LIk+74mzJjBbOf+/XE5Zwwjs5yZ+8zM+/l43I/7Pufc930+ZzHu63yu63N5m4QEcI3sV+c3cDigc2cYMsSUnVJCQ0RERDzMYVmWldaD8ufPn+x2fHw858+fJyAggODgYE6fPu2xALOzqKgo8uXLR2RkpMpPSZqVL2/Wu3YBhw5BmTKQmAgbNkCtWv95bECAWcfFZfC5+38CTz4JJUvC7t1JJxaxUbJ/G+mQkX8fGX1uERFvlluuXT3RlomLiyM4OJivv/7aPRIcoGvXrpw9e5bvv//+qmPuuOMO/P39+fnnn933LViwgHvvvZfY2FgCUnGdlVs+I2+X7Hpg925zR548EB0NPqmv6pzRa3ZPncNT53G/Lwv3wM03m/fi8GEoVixjwYmIiEi2lRnXr+maQ+PMmTPJlujoaHbs2EHjxo356quvPBKYiFzmww9NMqNp0+smMzyqe3fTADl0CL78MuueV0RERCSTeKItk575ABs1asTu3btxOp3u+3bu3EmxYsVSlcwQL+UqN1W+fJqSGTlauXLQoAE4nTBjht3RiIiISA7jsSuu8uXLM2LECPr27eupU4oIwIULcGmeGC4rOZAlgoLg2WfN9pgxkPYBXSIiIiJeLz1tmf79+zNx4kSmTp3K9u3b6d2791XzAQ4aNMi9f+/evTl9+jR9+/Zl586dzJ8/n7feeounnnrK469HspDmz0hZ50uTg0+bZm8cIiIikuN4tAuJn58fR48e9eQpReTbbyEyEkqVgnvvzfrn79HDJDb+/BN+/z3rn19EREQkC6S1LdO+fXveffddhgwZQs2aNdm0adNV8wEeO3bMvX/JkiVZtGgRa9eupXr16jzzzDP07duXF1980eOvRbLQzp1mrfkzkmvfHnx9Yf36pKSPiIiIiAeka1LwuXPnJrttWRbHjh3jo48+olGjRh4JTEQumTzZrLt3t2cYe4EC0KkTTJoEY8eC/o2LiIhINubJtkyfPn3o06dPio8tX778qvsaNmzIH3/8kabnEC+nERopK1QIWrSAH380ozSGDbM7IhEREckh0pXQuHziOwCHw0GhQoVo1qwZo0aN8kRcIgKUiN8PS5aAwwHdutkXyFNPmYTG11/D++/DpZ6HIl4vMRFWrYING2DTJti+nXkJYRzhRnjtJrjjDrM4HHZHKiIiWURtGfEoJTSurXPnpITGa6/pektEREQ8Il0JjcsnshORzPNg1BSz0by5KTlll9q1zcR+f/wBEyfCK6/YF4tIaiQkwMyZ8MYb8PffyR6627Ux9NK6QgXo1cskDW+4IetiFBERW6gtIx4TFQWusmIqOXW1Bx6AkBDYu9e0Ixo2tDsiERERyQFsqF8jIqnhsJw8eG6KufHYY7bGAphRGgDjx5sfi0W81aJFUKUKdOlikhn58sH998OQITBzJo/7TmKozzDzeGioqX393HNw883w+edgWXa/AhEREckOdu0y68KFITzc1lC8UkgIuEZEaXJwERER8ZB0jdDo379/qvd977330vMUIrlegwvLuDHhgPkx9orSCLZ46CHo3x8OH4a5c+HBB+2OSCQ5y4K33oLBg812gQIwYAD06QNhYe7dPu9i1kO/AKKjYcYMGDMGtmyBrl1h1iyTuCtRwp7XISIimUptGfEYlZu6vs6dTTJj5kxTutbf3+6IREREJJtLV0Jj48aNbNy4kfj4eCpeunjbuXMnvr6+1K5d272fQzUyRdLt/6ImmY1OnSBPHnuDAQgMhMcfh+HD4ZNPlNAQ73LunCkZ9e235navXvDuu5A3738fFxpqvtfdupn9X30V5s+H6tXhhx/gttsyO3IREcliasuIxyihcX133WUmCP/nH1i8GO691+6IREREJJtLV8mp++67j9tvv53Dhw+zYcMGNmzYwKFDh2jatCmtW7dm2bJlLFu2jKVLl3o6XpHcITKSu2Mu/TDrDeWmXHr2NOslS+DgQXtjEXE5dw6aNTPJjIAAmDDBjLC4XjLjcn5+8OKLsHEj1KkDp0+buWvmzs28uEVExBZqy4jHuBIamj/j2vz8oEMHs62yUyIiIuIB6UpojBo1iuHDh5M/f373ffnz5+eNN95g1KhRHgtOJNeaM4cg6yK7/SubH1e9RZky0LSpKeczdard0YjAxYumJNu6dVCwIKxYkZR4S48qVWDlSmjd2py7bVuYONFj4YqIiP3UlhGP2bnTrDVC47917mzWc+aYcp8iIiIiGZCuhEZUVBT//PPPVff/888/nDt3LsNBieR6M2cCMD9vB/C2cgfdu5v15MngdNobi+RuiYmmgbx0qRmNsXAhNGiQ8fMGB8N335nRUU6nKV81fnzGzysiIl5BbRnxCMtSQiO16tWDcuXg/Hn4/nu7oxEREZFsLl0JjbZt29K9e3e+/fZbDh8+zOHDh/nmm2/o0aMHD6quvkjG/PuvqS8LzA9tb3MwKWjXzvx4vG+f6ckuYpc+fZLKTH3/vWdHM/n5waefwvPPm9tPPpk0P4eIiGRrasuIJxRNPAIxMeDra0Yxy7U5HEmjNFR2SkRERDIoXQmNcePGcc8999CpUydKlSpFqVKl6NSpEy1btuTjjz/2dIwiuct330FCAtsDarAvwAt7ewUHJ9XBnTTJ3lgk12oT9TmMGwc+PvDVV6YUmqc5HPD226aEldMJHTtS7/xyzz+PiIhkKbVlxBNKx10anVG2rOlcIf/NldD46Sc4edLeWERERCRbS1dCIzg4mI8//ph///2XjRs3snHjRk6fPs3HH39MSEiIp2MUyV1c5aa8cXSGi2ui8q+/hqgoe2ORXKd03E6G/vOkufHaa5CZvWkdDvjkEzOXRlwc44/dT4XYLZn3fCIikunUlhFPKBt/aUJwlZtKnQoVoG5dUzL0UntHREREJD3SldBwOXbsGMeOHaN8+fKEhIRgWZan4hLJnU6eNPMBAD/m9eKERv36ULkyXLigBolkrdhYPjjenhArxozKGDQo85/T1xemT4c77iDUOscnx9rAmTOZ/7wiIpKp1JaRjCgTp4TGlY4cgQMHoHz5lJc395tRGpuen3bNfVxLRITNL0ZERES8VroSGv/++y/NmzenQoUK3HvvvRw7dgyAHj16MGDAAI8GKJKrfPONKW1Tty6H/MvaHc21ORzJJwcXSaOIiP9uxF5rmVpkIFXiNvEPBeHLL02yISsEBcE333DIrzQ3Jew1ZRMSE7PmuUVExKPUlhFPKOMaoVGhgr2BeJGEBIiPv/bj80I7kIgPNWNXc1Pc7mvut3+/SYyIiIiIpCRdCY1nn30Wf39/Dh48SHBwsPv+9u3bs3DhQo8FJ5LrzJhh1u29eHSGS5cuZv6CVatg7167o5Fs5sAB01hNi3rnl9M1cgwAPXynQvHing/sv9xwA08V+44LjjywYAG8+mrWPr+IiHiE2jLiCWVcc2hohEYy/v6wa1fKy6p9RfG92wy9WNJj+jX3K13a3tcgIiIi3s0vPQf99NNPLFq0iBtvvDHZ/eXLl+eAulKIpM/Ro/DLL2b74YdhvH2hHDlieliVL/9fexVjSmAzGl34mfdu/YpPCrzsfqRUKfj550wPU7K50qVNozVVYmOh+v8AGO/zBAt97s20uP7L9sCavFz4U9470RnefBNuvRUeeMCWWEREJH3UlpGMCrBiKZGw39xQQiNtOnc2E4NPmwaDB5uR3yIiIiJpkK4RGjExMcl6M7mcPn2awMDADAclkivNmQOWBQ0awE032RrK9YaLu/yQtxMA95+bZmJHQ8Qlk7z9NuzcCUWL8orPcFtD+SFvJ+jXz9x47DE4fNjWeEREJG3UlpGMuiluN744IW9eKFLE7nCyl7ZtIU8ec123fr3d0YiIiEg2lK6ERpMmTfj888/dtx0OB06nk5EjR9K0aVOPBSeSq3z3nVk/+KC9cVzyX8PFXcuInQ9CYCA3x29n19d/aoi4ZI6dO+Gtt8z26NFEOsJtDQcwCZa6deH0aVN+TfNpiIhkG2rLSEaVib+s3JRGGKRN3rxw//1me9o0e2MRERGRbCldCY2RI0cyYcIE7rnnHuLi4hg4cCBVq1Zl5cqVvP32256OUSTnO3MGli83223a2BlJ2uTLB61bm+3p0+2NRXImy4LevU3JqRYtTDk2bxAQYL7zISGwYgUMt3fUiIiIpJ7aMpJR7gnBVW4qfTp3NuuvvjJDw0VERETSIF0JjapVq7Jz504aN27MAw88QExMDA8++CAbN26kXLlyno5RJOebP99czN9yy/UmrvA+nUzZKb76CpxOe2ORnOfrr2HpUggKgo8/9q5ekOXLm5gAhg6F33+3NRwREUkdtWUko8rGKaGRIS1aQIECcOKEuc4TERERSYM0TwoeHx9Py5YtGTduHC+//PL1DxCR63OVm2rb1t440uPee81IjcOHL01qfofdEUlOERcHgwaZ7RdegLJl7Y0nJY88AosWmdEajz4Kf/5pRm2IiIhXUltGPME9QqNCBXsDya4CAsyo23HjTNmpu++2OyIRERHJRtI8QsPf35/NmzdnRiwiudOFC7BwodnOTuWmXIKCoF07s62yU+JJn3wCe/ZA0aLw3HN2R5Myh8OM0rjxRhOrKwEjIiJeSW0Z8YTScZfNoSHp06WLWX/7LZw/b28sIiIikq2kq+RUly5d+Oyzzzwdi0jutHixuYgvWRJq17Y7mvRx1cGdPRt/K87eWCRnOHsWhg0z28OGQWioreH8p3z5wPV/4ocfJs2HIyIiXkltGcmQf/+lgPNfs53dSsV6k9tug9KlIToafvjB7mhEREQkG0lzySmAhIQEJk2axM8//0ydOnUIuaK8xnvvveeR4ERyBVe5qTZtvGt+gLS44w4oVgyOHaNJ0EKWhtxvd0SS3Q0fDqdPQ5Uq0L273dFc3913Q69eMGGCiXfzZsib1+6oREQkBWrLSIbsMOWmjvqVpLjKTKafw2Hm4nvrLVN2qn17uyMSERGRbCJNCY29e/dSunRptm7dSu1LPcl37tyZbB9Hdv1BVsQOCQlJPZKy4/wZLr6+0KEDvP8+952broSGZMzBg/DBB2Z75EjwS1fuPeu9+66ZT2P/fhg40JTMEhERr6G2jHjEpe/MPv8KFLc5lGyvc2eT0FiwAP79F264we6IREREJBtI069E5cuX59ixYyxbtgyA9u3bM2bMGIoUKZIpwYnkeL/+ai7eCxSAJk3sjiZjOneG99+necxcQpznAPVOl3R6/XWIjYWmTc2k89lF3rwwaRI0b24muXzwQbjrLrujEhGRS9SWEY+4NEJjX0BFGtkcSrZXpQrUrAmbNsHs2fDEE3ZHJCIiItlAmubQsCwr2e0FCxYQExPj0YBEchXX6IzWrbNPL/RrqV0bKlQgj3WBiOg5dkcj2dW+fTBlitl+443sV4atWTN46imz3aMHREXZG4+IiLipLSMe4Upo+GtCcI9wzcX35Zf2xiEiIiLZRromBXe5slEgImk0f75Zt25tbxye4HC4GyT3R0+zORjJtt54w5Riu/tuM1lkdjRiBJQtC4cOwYABdkcjIiLXoLaMpIs7oVHB5kByiI4dTTvit99MxxYRERGR60hTQsPhcFxVV1Z1ZkXSac8e0yDy8zM/3uYEHTsCcNv5n+HECZuDkWxnzx6YOtVsv/aavbFkRGgoTJ5stj/9FBYutDceEREB1JYRD0hMhN27AVNySjygRAkzwhXg88/tjUVERESyhTTVuLEsi27duhEYGAjAxYsXeeKJJwgJCUm237fffuu5CEVyKtfojMaNIV8+e2PxlPLl+TPwVmrErjV1cPv0sTsiyU7eeMP8UNCyJTRoYHc0GXP77dC3r5nc/PHHYetWCA+3OyoRkVxNbRnJsAMHIC6OWEcgR/1usjuanKN7d1iyxJQdHTyYDBaSEBERkRwuTQmNrl27JrvdpUsXjwYjkqvMm2fWrVrZG4eH/ZC3s0loTJumhIak3u7d8MUXZnvoUFtD8Zi33jKJy927Tempzz6zOyIRkVxNbRnJsEvlpvb7l8fp8LU5mBykbVsIC4P9+2H5cqCZzQGJiIiIN0tTQmOyq4SGiGRMdDSsWGG2c1hCY35oewad6o/vH3/A3r1mLgGR6xk+3IzOuOceqF/f7miu6cgRM8VH+fKp2TuYuhcmMY078Jk0ie4LHubXkBaUKgU//5zZkYqIyJXUlpEM27kTgP2aP8OzgoNN6drx4y+V7VRCQ0RERK5NYzlF7PDzzxAXZ37sr1TJ7mg86pRfUf7Ic6kRMmOGvcFI9nD4cNLojMGD7Y3lOhISID4+9fuvy9OEL/I9DcCb//Tk331RHDiQScGJiIhI5ro0QmOv5s/wvO7dzfqbbwhNjLQ3FhEREfFqSmiI2ME1f0arVpADJ6Ocl7eD2VBCQ1Jj1CiTJbjjDmjY0O5orsvfH3btSv3S9chbULYsxRMO8XHI83aHLyIiIul1KaGxz18JDY+rVw+qVIELF2gVPdPuaERERMSLKaEhktUsC3780WznsHJTLj+FPGh+9d2yBbZtszsc8WanTsGECWZ70CB7Y8ksISHu+TM6RE2g4fklNgckIiIi6eJOaKjklMc5HO5RGu2iVB5ORERErk0JDZGstmkTHD1qasXecYfd0WSKKN/8Zi4EgK++sjcY8W4ffgjnz0OtWnD33XZHk3nuvBN69wbgrZM94Nw5e+MRERGRtImONpNpAftUcipzPPII+PpSK/YPysVttzsaERER8VJKaIhktXnzzDoiAoKC7I0lM3W4VHbqq6/MqBSRK4Q4z5mEBsCLL+bI8mvJvP02h/1KcWPCAfN6RUREJPvYtcusCxYk0reAvbHkVEWKQOvWADwU9anNwYiIiIi3UkJDJKtdPn9GTnb//WYUyt69sG6d3dGIF3o4ciKcOQPly0O7dnaHk/ny5uXlwpca5x9/DMuX2xqOiIiIpMGlclNU1OiMTNWzJwAPRk2BixftjUVERES8khIaIlnpn39gzRqzndMTGiEhJqkBKjslV/Gz4uke+b65MXAg+PraG1AW+T04ghlhpqFOjx4QE2NvQCIiIpI6O3eadQXNn5GpWrbkqF9J8jtPw7ff2h2NiIiIeCElNESy0oIFpvxSzZpQooTd0WS+jh3NeuZMSEy0NxbxKi2jv6ZYwmEoXBi6dLE7nCz1dsF3oWRJM3rpuefsDkdERERSQyM0soavL7PDHjfb48fbG4uIiIh4JSU0RLJSbik35dKiBYSHm0nQf/nF7mjEW1gWj50dZbb79MnZc8mkINonDCZPNjfGjYMff7Q3IBEREbk+JTSyzOywHiTiAytXwt9/2x2OiIiIeBklNESySnw8LFpktnNLQiMwEB580GzPmGFvLOI9fvmFarHruegIgieesDsaezRvDv36me3HHjPl6ERERMQ7WVZSQkMlpzLdCb8SLAsxk4MzYYK9wYiIiIjX8YqExtixYyldujRBQUHUr1+fNa45Bq5h9uzZVKpUiaCgIKpVq8aPV/RutSyLIUOGUKxYMfLkyUNERAS7du1Kts/p06fp3LkzYWFhhIeH06NHD6Kjo1N8vt27d5M3b17Cw8Mz9Doll/vtN4iMhIIFoV49u6PJOq6yU19/bZI6IqPM6Izv8naFQoVsDsZGb70FVarAiRPQq5f5sURERES8z/HjEB0NPj5Qrpzd0eQKM8L+ZzamTtXk4CIiIpKM7QmNmTNn0r9/f1599VU2bNhAjRo1aNGiBSdPnkxx/99//52OHTvSo0cPNm7cSJs2bWjTpg1bt2517zNy5EjGjBnDuHHjWL16NSEhIbRo0YKLl10Ide7cmW3btrF48WLmzZvHypUr6dWr11XPFx8fT8eOHWnSpInnX7zkLq5yUy1b5poJkAFo2hSKFIF//4XFi+2ORuy2cyf88AMAU8L72RuL3fLkgS+/BH9/mDPHNNhFRETE+7hGZ5QpY0YgS6b7JbgF3HQTnD5tOkaJiIiIXGJ7QuO9996jZ8+edO/enSpVqjBu3DiCg4OZNGlSivt/8MEHtGzZkueff57KlSvz+uuvU7t2bT766CPAjM4YPXo0r7zyCg888ADVq1fn888/5+jRo8yZMweA7du3s3DhQj799FPq169P48aN+fDDD5kxYwZHjx5N9nyvvPIKlSpV4uGHH87U90FyAVdCo3Vre+PIar6+8NBDZvurr+yNRez3wQdgWSwLbsXegEp2R2O/WrVg2DCz/cwzsG+fvfGIiEiapHWkucuMGTNwOBy0adMmcwMUz9D8GVnO6fCFxy9NDj52rL3BiIiIiFexNaERFxfH+vXriYiIcN/n4+NDREQEq1atSvGYVatWJdsfoEWLFu799+3bx/Hjx5Ptky9fPurXr+/eZ9WqVYSHh1O3bl33PhEREfj4+LB69Wr3fUuXLmX27NmMTeUFVGxsLFFRUckWEcD8SLl9u/lxv0ULu6PJeq6yU3PmwIULtoYiNvr3X/dk2J+FD7A5GC/y/PPQuDGcOwePPgqJiXZHJCIiqZDWkeYu+/fv57nnntMI8Oxk506z1vwZWatXLzOS9Y8/YO1au6MRERERL2FrQuPUqVMkJiZSpEiRZPcXKVKE48ePp3jM8ePH/3N/1/p6+xQuXDjZ435+fhQoUMC9z7///ku3bt2YMmUKYWFhqXo9w4cPJ1++fO6lZMmSqTpOcgHX6IxGjSA3zsXSsCGUKmVqD7veC8l9xo83Ca2aNVmd5067o/Eevr7w+ecQGgq//grvvmt3RCIikgppHWkOkJiYSOfOnXnttdcoW7ZsFkYrGaIRGvYoUgTatzfbH35obywiIiLiNWwvOeWtevbsSadOnbj99ttTfcygQYOIjIx0L4cOHcrECCVbcf2I36qVvXHYxeGADh3MtspO5U6xsUkN0QEDzHdCkpQpA2PGmO3Bg2HjRnvjERGR/5SekeYAw4YNo3DhwvTo0SMrwhRPUULDPk8/bdYzZ8J1Rj+JiIhI7uBn55MXLFgQX19fTpw4kez+EydOULRo0RSPKVq06H/u71qfOHGCYsWKJdunZs2a7n2uHAqekJDA6dOn3ccvXbqUuXPn8u6lnrKWZeF0OvHz82PChAk89thjV8UWGBhIoCaJkyvFxMCyZWY7tyY0wCQ03n7bJHciIyFfPrsjkqw0YwYcPw7Fi8PDD8Nrdgfkhbp1MxOmf/ed6Y24fj3kzeuRU0dEwIEDGTtHqVLw888eCUdEJNv7r5Hmf//9d4rH/Prrr3z22Wds2rQp1c8TGxtLbGys+7ZK2trg4kXYu9dsV9L8X1muXj2zrFkDEybAK6/YHZGIiIjYzNYRGgEBAdSpU4clS5a473M6nSxZsoSGDRumeEzDhg2T7Q+wePFi9/5lypShaNGiyfaJiopi9erV7n0aNmzI2bNnWb9+vXufpUuX4nQ6qV+/PmDm2di0aZN7GTZsGHnz5mXTpk20bdvWM2+A5A5Llpje6aVKQZUqdkdjnxo1TCMwNtbMpSG5h2XBqFFm+5lnICDA3ni8lcMBEydCyZKwaxc88YR57zzgwAHYvz/9x+/fn/GEiIhIbnbu3DkeeeQRJk6cSMGCBVN9nEraeoGdO8HpNGVjr9HpTjLZM8+Y9SefQHy8vbGIiIiI7WwdoQHQv39/unbtSt26dalXrx6jR48mJiaG7t27A/Doo49SokQJhg8fDkDfvn254447GDVqFK1atWLGjBmsW7eOCRMmAOBwOOjXrx9vvPEG5cuXp0yZMgwePJjixYvTpk0bACpXrkzLli3p2bMn48aNIz4+nj59+tChQweKFy/u3udy69atw8fHh6pVq2bROyM5xuXlpnJzmR2HAzp1giFD4MsvoWtXuyOSrLJkCWzZAiEhZnJHubYbbjCjWW6/HaZPh6ZN4fHHPXLq0qVNniQ9ypf3SAgiIjlGWkea79mzh/3793Pfffe573M6nYCZy2/Hjh2UK1fuquMGDRpE//793bejoqKU1Mhq27ebdeXKufta3k4PPWRKlh49akayPvyw3RGJiIiIjWyfQ6N9+/a8++67DBkyhJo1a7Jp0yYWLlzoHr598OBBjh075t7/tttuY/r06UyYMIEaNWrw9ddfM2fOnGSJhoEDB/L000/Tq1cvbr31VqKjo1m4cCFBQUHufaZNm0alSpVo3rw59957L40bN3YnRUQ8xrLgxx/NduvW9sbiDTp3NuslS0yDRHIH1+iMxx6D/PntjSU7uO02ePNNs/300yYZJCIiXiWtI80rVarEli1bko0Av//++2natCmbNm26ZpIiMDCQsLCwZItkscsTGmKPgAD43//M9nvveWwEq4iIiGRPDsvS1UBmiYqKIl++fERGRqrxkVv9+SfUrAl58sC//5p1Krl6RKenR7Wrok9cXNqPzejx1427USP4/Xd4913T00pytm3boGpV06Nx1y641Ps0u36/M/rcqX7dTqdJgi5YABUqmLrRGZh3JiPvtyeOF5HsQdeuaTNz5ky6du3K+PHj3SPNZ82axd9//02RIkWuGml+pW7dunH27FnmpKEUpz4jG7RvD7NmwTvvwHPPAZ77fzGj1xWeOoe3xZLi+3vihCnhGxsLv/wCjRtn7ElEREQkS2TG9avtIzREcjRXuanmzdOUzMjRHnnErL/80t44JGuMHm3Wbdu6kxmSCj4+8PnnZj6NnTtNibZLpUlERMQ7pHWkuWRTGqHhHYoUSSpZO3KkvbGIiIiIrZTQEMlM8+aZdatW9sbhTR56CPz9YdMm2LrV7mgkM504AV98YbYvq/8tqVSwIHz7LQQGwvffwzV6+IqIiH369OnDgQMHiI2NZfXq1dSvX9/92PLly5kyZco1j50yZUqaRmeIDRITTccCUELDGwwYYEb9/vBDUqJJREREch0lNEQyy6lT8McfZvvee+2NxZvccEPS+zFtmr2xSOb65BNTFqBePTMvhKRd3bowdqzZHjwYFi60Nx4REZHcZN8+cy0TFGTKHYm9KlSANm3MtmuONhEREcl1lNAQySwLF5oJ66pVg5tusjsa79Kli1lPm6YyOjnVhQtJP8S7etNJ+vToAb16mb8nHTrAjh12RyQiIpI7uEYBVKwIvr72xiLG88+b9RdfgEq6iYiI5EpKaIhkFtf8Ga1b2xuHN2rdGsLC4NAhM6mf5DxffmlGKZUqBQ8+aHc02d+YMdCwIURGwv33w5kzdkckIiKS8/31l1mr3JT3aNgQGjUyM4+PGWN3NCIiImIDJTREMkN8fFJpGM2fcbWgIDOXBsDUqfbGIp7ndMJ775ntvn3Bz8/eeHKCwED47jsz2mvnTnj4YfN3RkRERDKPa4RGlSr2xiHJDRxo1h9/rE4eIiIiuZASGiKZ4bff4OxZM19EgwZ2R+OdunY169mzISbG3ljEs378Ef7+G/LmNeWSxDOKFIG5cyEkBH7+Gfr1M2WoREREJHO4EhoaoeFdWreG6tUhKgpGj7Y7GhEREcliSmiIZIYffjDre+9Vvd1radwYypaF6Gj45hu7oxFPeucds37iCVNaTDynRg1TzgtMr0TXey0iIiKeZVlKaHgrHx8YMsRsf/CB6UgmIiIiuYYSGiKZYd48s77vPnvj8GYOB3TrZranTLEzEvGkNWtg5Urw9zflpsTz2rRJKun1wgtmUkwRERHxrKNH4dw50zmpfHm7o5ErtW0LVaua+cU0SkNERCRXUUJDxNN27jSLvz+0aGF3NN6ta1eT2Fi2DPbtszsa8QTXiIFOnaBECXtjycmefRYGDDDbjz0GixbZG4+IiEhO4xqdUa4cBATYG4tczccHBg8226NHa5SGiIhILqKEhoinucpN3XGHyu1cz003QbNmZvvzz+2NRTJu9+6k8mHPPWdvLLnByJEmcZSQAO3awfr1dkckIiKSc6jclPf7v/8zE7ZHRsKYMXZHIyIiIllECQ0RT3MlNFq3tjeO7KJ7d7OeMgWcTltDkQx67z1Tb/qee0wJAMlcPj4weTI0bw4xMWbOnj177I5KREQkZ1BCw/tdPkrj/ffhzBl74xEREZEsoYSGiCedOQO//mq2NX9G6rRta0ay7N9v5l6Q7Omff8yP6wDPP29vLLlJQAB8+y3UrAknT0LLlmYtIiIiGaOERvbw0EOmI83ZszBihN3RiIiISBZQQkPEkxYuhMREM/S5bFm7o8kegoOhfXuzPWmSvbFI+o0dCxcvQp06cOeddkeTu4SFwY8/QunSpuxX69ZmElMRERFJv7/+MmslNLybr29SIuODD+DQIXvjERERkUynhIaIJ7nKTWl0Rto89phZz56toeLZ0fnz8NFHZvv5581E75K1ihUzCdUbboC1a+H+++HCBbujEhERyZ5OnUoa8aiEhve79164/XaIjYWhQ+2ORkRERDKZn90BiOQY8fGwYIHZvpTQiIiAAwfSd7oDB8Avt/wLrV8fqleHzZvhiy/gmWfsjkjSYvJk+PdfKFPGTE4t9qhY0fwNat4cli+HBx+EOXMgMNDuyERERLKXLVvMukwZCA21N5Zc6MgRSEiA8uVTe4SDmhffZjYNSZw0hdaL+7M78BZKlYKff87MSEVERMQOGqEh4im//WZqt95wAzRoAJikxP796TtdfLy5kM8VHA7o1ctsjx9vJpaW7CEx0UwGDtC/fy7KwnmpW2+F+fMhTx4zYqNjx1z0h0RERMRDXAmNatXsjSOXSkgwbaG02BTUgEUhD+KLkwGnX2L//vR3LBMRERHvpl+eRDxl3jyzvvdeU8v1ktKlYdeutJ8uIMAzYWUbXbrAwIGmXvHvv0OjRnZHJKnx7bewdy8UKADdu9sdjQA0aQLff2/m0vjuO+jaFR/rc5wO3+sfKyIiIkpoeAF//3S0oXa8Bbd8T0TMXNoXX8ZqmmZKbCIiImIvjdAQ8RTNn5Ex+fJBhw5me/x4e2OR1LEseOcds/3UUxASYm88kuSuu+Drr82ImenTGfbPExr5JCIiklpKaGRPFSvCE08AMPjUM/haGqUqIiKSE2mEhogn7NxpFj8/aNHC7miyr169YNIkmDULRo82vf7Fe61caSagDgqCPn3sjibHS/ucPPfRsuB0Rh/vQPuoTzkVEwzWaE3aLiIi8l+cTti61WwroZH9DBsGX31FxdNb6Rg5DtA1qoiISE6jERoinuAanXHHHRAWZm8s2Vm9elCjBsTGmsnBxbu98YZZd+sGhQvbGkpukJ45eRaGPsSgwpMAeCpxDLz0kkZqiIiI/Jf9+yEmxtR/Tf2s1OItChSAN98EoN/pwXDqlM0BiYiIiKcpoSHiCa75M1RuKmMcDvjf/8z2uHGmh5x4pz/+gJ9/NqOSXnjB7mhyDdecPGlZRp7oylM+n5gTjBhhei6KiIhIylzlpipXNhM5SPbTsyfbA2qQz3kWXnnF7mhERETEw5TQEMmoM2fgl1/MthIaGdelC+TNC3//DYsX2x2NXItrdMYjj5hf2cWrTfR9gud83jM3hg41iQ0RERG5mubPyP58fRlW6EOzPWECrFtnbzwiIiLiUUpoiGTUwoWQmAhVqkDZsnZHk/3lzQuPPWa2P/jA3lgkZRs3wvz54OMDgwbZHY2k0hjfZ2H4cHNj0CAzT42IiIgkp4RGjrAuTxPmhnYypTZ79oT4eLtDEhEREQ9RQkMko1zzZ2h0huc8/bQpP7VgAezYYXc0ciXX6IwOHVRbOrt58UV49VWz/eyz8Mkn9sYjIiLibZTQyDHeLPg+5M8PmzbB++/bHY6IiIh4iBIaIhmRkGB+dAdo3dreWHKScuWS3s8PP7Q3Fklu2zb49luTcHr5ZbujkfR49dWkeU+efBImTbI3HhEREW8RGws7d5ptJTSyvdN+hWHUKHNj6FDYs8fWeERERMQzlNAQyYiVK+HsWbjhBmjY0O5ocpa+fc16yhTzHot3cE0o/eCDpsyaZD8Ohyk91a+fuf344zB9uq0hiYiIeIXt200p2fBwKFHC7mjEE7p1g2bN4MIFeOIJU4JKREREsjUlNEQy4ttvzbpNG/D1tTWUHKdZM7jlFoiJUQ9yb7FpE8yaZX4Qd5UtkuzJ4YD33oPevU3D/tFH4euv7Y5KRETEXlu3mnW1aub/Ssn+HA4YPx6CguDnn2HyZLsjEhERkQxSQkMkvZzOpITGgw/aG0tO5HDAM8+Y7Q8/NOW9xF5Dhph1hw4qw5ATOBzw0UfQvbvpjdqxI8yda3dUIiIi9tH8GTnTzTfDa6+Z7X79YP9+O6MRERGRDFJCQyS9Vq+GY8cgb15o3tzuaHKmLl2gYEHT6Jg1y+5ocrfVq+GHH8xIpKFD7Y5GPMXHByZOhE6dTNLwoYdg4UK7oxIREbGHEho514AB0KgRnDtnylA5nXZHJCIiIumkhIZIerlGZ7RuDYGB9saSUwUHJ82lMWKEat7a6ZVXzLprV6hQwd5YxLN8fWHqVGjXDuLioG1b+OUXu6MSERHJekpo5Fyu652QEFixAj74wO6IREREJJ2U0BBJD8tSuams8tRTEBpqGpjz59sdTe60fLmpOezvD4MH2x2NZAY/PzMxeOvWcPGiWW/YYHdUIiIiWefMGTh82GxXrWpvLJI5ypUzc4gBDBoE27bZG4+IiIikixIaIumxeTPs3Wsml7vnHrujydny54cnnzTbb72lURpZzbJMgw+gVy8oXdrWcCQTBQSY0m533AFRUdCyJezYYXdUIiIiWWPTJrMuXRry5bMzEslMPXua9ltsrJkX7vx5uyMSERGRNFJCQyQ9XKMzWrY0w5Ylcz37rCnrtWoVrFxpdzS5y+zZ8Mcf5nv+8st2RyOZLU8eMzF4nTrwzz9w111w8KDdUYmIiGS+9evNuk4de+OQzOVwwOTJUKQIbN2aVN5WREREsg0/uwMQyZZUbiprFS0K3bvDuHEwfLjpQS6ZLzYWXnzRbL/wAhQrZm88kjXCwszE4E2awN9/w113EXf4F44lFqZ8+fSdslQpU7VMRETEaymhkXsUKQLTppmOG59+Ck2bQqdOdkclIiIiqaQRGiJptXOn6c3j52fqzEvWeP55M5nfokVmxIBkvg8/hH37oHhxGDDA7mgkKxUsCIsXm0zEzp18f7EFwfFn03Wq/fvhwAGPRiciIuJ5SmjkLs2bwyuvmO3//c+08URERCRb0AgNkbSaNcusmzc38ztI1ihbFh591AwRf/llWLLE7ohytn//hTfeMNtvvgnBwVftEhGR/h+qDxwwOUHxYjfeaJIaTZpQ88Qm5jta0+jPn1L8LvyX9I7qEBERyTKRkbBrl9muXdveWCTrvPqqKWe7YgW0a2fK24aG2h2ViIiIXIdGaIik1YwZZt2hg71x5EavvmomLl66VPVrMtuwYaZxX7OmSSSl4MAB0/s+PeLjISEh3dFJVilfHhYt4gzhNLJ+g44d9cGJiEjOs3GjWd90kxmlKLmDry9Mn27K227dCt26gWXZHZWIiIhch/rHiqTF1q2wbZv5Ub1NG7ujyX1KlYInnoAxY+Cll8woGYfD7qhynj//hLFjzfaoUeBz7dx36dJJHRrTIiAgfaFld0eOmHxAekYt2DaqpUYN2vr+wILEu8gzdy48+SSMH69/eyIiknNs2GDWKjeVY6T+mqs4tXy/4UvuJOCbbxhVcDjjCryUbA/NBSYiIuJdNEJDJC1mzjTre+6B8HBbQ8m1XnoJQkJg7VqYM8fuaHIep9P8YJ2YCA89BM2a2R1RjpKQYEanpIedo1p+92nMI75fmeTWxIlmBI+IiEhOofkzcpy0XHNtzHMbrxUynXmePf0Kd8bMdz+mucBERES8j0ZoiKSWZSWVm2rf3t5YcrMiRaBfPzOvwyuvwP33m+Hi4hlTpsDvv5v6we+/b3c0OZK/f/Yc1TLXpw2MGQu9e8PQoVCsGPTqZW9QIiIinqCERo6UtmuuntB7Az7jxjHxXAf48ReoWVNzgYmIiHghjdAQSa0NG2D3bsiTB+67z+5ocrfnnjMTsv/1F0yaZHc0Oce//8LAgWb7tdegRAl74xHv88QTMHiw2e7dG+bOtTceERGRjDp3DnbuNNtKaORuH3wATZtCdDS0agWHDtkdkYiIiKRACQ2R1HKVm7rvPtN7XewTHg5Dhpjtl16CM2dsDSfHePFFk9SoVg2eftruaMRbvfYa9OhhypO1b29G9IiIiGRXGzeakdglS0KhQnZHI3YKCIBvv4UqVeDoUWjVitDESLujEhERkSsooSGSGk5nUkJD5aa8w1NPmcbGqVNJyQ1JvyVL4NNPzfbHH5sx+iIpcThg3Dho3RouXjRJ3u3b7Y5KREQkfVwTgteubW8c4h3Cw+HHH6FoUdiyhY+OtyPAedHuqEREROQymkNDJDX++AMOHoS8ec2E4JIpIiLSMumePw3Of8gXNCfxo4954Pue7AisTqlS8PPPmRllDhQVBY89ZrZ794bGje2NR7yfn5+ZU6h5c1i9Glq2hFWroHhxuyMTERFJG82fIVcqVQrmz4fbb6dRzBI+ONEe4r9Whx8REREv4RUjNMaOHUvp0qUJCgqifv36rFmz5j/3nz17NpUqVSIoKIhq1arx448/JnvcsiyGDBlCsWLFyJMnDxEREey6Yjaw06dP07lzZ8LCwggPD6dHjx5ER0e7H1++fDkPPPAAxYoVIyQkhJo1azJt2jTPvWjJXr74wqzbtDFzaEimOHAA9u9P/f5/BDdjQcj/4YuTIf88zf59VhoSIuI2YIBJ2JUtCyNH2h2NZBchITBvHlSoYL4/LVvC2bN2RyUiIpI2SmhISmrXhh9+4KIjiIiYufDoo5CYaHdUIiIighckNGbOnEn//v159dVX2bBhAzVq1KBFixacPHkyxf1///13OnbsSI8ePdi4cSNt2rShTZs2bN261b3PyJEjGTNmDOPGjWP16tWEhITQokULLl5MGirauXNntm3bxuLFi5k3bx4rV66kV69eyZ6nevXqfPPNN2zevJnu3bvz6KOPMm/evMx7M8Q7XbxoeiKDuZCVTFW6NOzalfrlnr9GQZ481Lu4kr43fGl3+NnPggWm1JTDAZMna34YSZuCBWHRIndZBtq2hdhYu6MSEckyaemYNXHiRJo0aUL+/PnJnz8/ERER1+3IJZksOhr+/ttsK6EhV2ralD5FvyEOf9Me7NnTlCIWERERW9me0Hjvvffo2bMn3bt3p0qVKowbN47g4GAmTZqU4v4ffPABLVu25Pnnn6dy5cq8/vrr1K5dm48++ggwozNGjx7NK6+8wgMPPED16tX5/PPPOXr0KHPmzAFg+/btLFy4kE8//ZT69evTuHFjPvzwQ2bMmMHRo0cBeOmll3j99de57bbbKFeuHH379qVly5Z8++23WfK+iBf54QfT67hkSWja1O5o5Eo33QSDBwPwyqm+FE44anNA2cipU/D442a7b1+4/XZ745HsqXRpkxjLmxeWL4dHHlEPRhHJFdLaMWv58uV07NiRZcuWsWrVKkqWLMndd9/NkSNHsjhycdu0yUwIXqIEFClidzTihVaE3MuAotPBx8d0/unaFRIS7A5LREQkV7M1oREXF8f69euJiIhw3+fj40NERASrVq1K8ZhVq1Yl2x+gRYsW7v337dvH8ePHk+2TL18+6tev795n1apVhIeHU7duXfc+ERER+Pj4sHr16mvGGxkZSYECBa75eGxsLFFRUckWyQGmTjXrRx4BX197Y5GUPf881KlDuPMMr5/8n2mYyn9zOs2Io6NHoWJFePNNuyOS7KxmTZgzx9SWnj0b+vXTv0MRyfHS2jFr2rRpPPnkk9SsWZNKlSrx6aef4nQ6WbJkSRZHLm6//27W9erZG4d4tYWh/wfTppm24JdfwsMPa0SqiIiIjWxNaJw6dYrExESKXNEbpkiRIhw/fjzFY44fP/6f+7vW19uncOHCyR738/OjQIEC13zeWbNmsXbtWrp3737N1zN8+HDy5cvnXkqWLHnNfSWbOH4cFi402yo35b38/GDKFOIIoNn5eUlznsi1vf226VUfFASzZkFwsN0RSXbXrJn5t+dwwEcfwfDhdkckIpJp0tMx60rnz58nPj5eHabs9OuvZt24sb1xiPfr0AG++QYCAuC77+CBB0zJMhEREclytpecyg6WLVtG9+7dmThxIrfccss19xs0aBCRkZHu5dChQ1kYpWSKadNM6ZQGDUwvdvFeVasypsBQs/3MM6DyDde2YgW88orZHjsWqle3Nx7JOdq3h9GjzfbLL8M1eimLiGR36emYdaUXXniB4sWLXzX6/HLqMJWJnM6kERpKaEhqPPAAzJtnOgItWmTKtarNISIikuVsTWgULFgQX19fTpw4kez+EydOULRo0RSPKVq06H/u71pfb58ra9smJCRw+vTpq553xYoV3Hfffbz//vs8ep0e+oGBgYSFhSVbJBuzrKRyU1272huLpMqn+Z/nz8BbITISOndWfduUHD1qepg5neZ7/R+jzkTS5Zln4MUXzXavXjSNmWdvPCIiXmjEiBHMmDGD7777jqCgoGvupw5TmWjHDvj3X8iTB2rVsjsayS7uuguWLIFChWDjRqhf38zFIiIiIlnG1oRGQEAAderUSVY31lVHtmHDhike07Bhw6vqzC5evNi9f5kyZShatGiyfaKioli9erV7n4YNG3L27FnWr1/v3mfp0qU4nU7q16/vvm/58uW0atWKt99+m169emX8BUv2smkTbNkCgYGm17F4vUSHH88X+QJCQ80ohJdftjsk7xIdDa1bm1Jqt9xiRmc4HHZHJTnRW29Bt26QmMgHxx+m5oXUlV8REcku0tMxy+Xdd99lxIgR/PTTT1S/zihJdZjKRL/9Ztb16pk5oERSq0ED+OMPqFzZjNBo3NjMISYiIiJZwvaSU/3792fixIlMnTqV7du307t3b2JiYtxzVTz66KMMGjTIvX/fvn1ZuHAho0aN4u+//2bo0KGsW7eOPn36AOBwOOjXrx9vvPEGc+fOZcuWLTz66KMUL16cNm3aAFC5cmVatmxJz549WbNmDb/99ht9+vShQ4cOFC9eHDBlplq1asUzzzxDu3btOH78OMePH+f06dNZ+waJfVylUu6/H/LntzcWSbV9ARWTPruRI81ExWJKp3XqZHqSFSoEc+dCSIjdUUlO5XDAhAlw773ksS4w4Vhr2L7d7qhERDwmPR2zAEaOHMnrr7/OwoULqVu3blaEKtei+TMkI8qWNSXLIiIgJsZMFN6vH8TF2R2ZiIhIjudndwDt27fnn3/+YciQIRw/fpyaNWuycOFCdz3agwcP4uOTlHe57bbbmD59Oq+88govvfQS5cuXZ86cOVStWtW9z8CBA4mJiaFXr16cPXuWxo0bs3DhwmTDuadNm0afPn1o3rw5Pj4+tGvXjjFjxrgfnzp1KufPn2f48OEMv2xi0zvuuIPly5dn4jsiXiE6Gj7/3Gz37GlvLJJ2Dz1kGhSjR5uySuvXw8032x2Vvfr3hx9+MJOAz51rGmEimcnfH2bNYtMNzakZuxpatDAN/xtvtDsyERGP6N+/P127dqVu3brUq1eP0aNHX9Uxq0SJEu62xNtvv82QIUOYPn06pUuXds+1ERoaSmhoqG2vI9dyjdBo1MjeOCT7Cg+HBQvM3HRvvw0ffACrV8OMGVCqVLJdIyLgwIGMP2WpUvDzzxk/j4iISHbmsCzLsjuInCoqKop8+fIRGRmp4eHZzcSJ0KuX+RF8xw7wSd9gpvLlzXrXrrQfGxBg1unp5JORYzN6fEZec0aOver4+Hho2tQ0VitWNOsbbkjfibO7t95KKr81a5ZJ+HhAbvx+Z9fntjPuemVP8dXhxpSL32FKnf3yi0a9iXgpXbum3UcffcQ777zj7pg1ZswYdwnbO++8k9KlSzNlyhQASpcuzYEUftF89dVXGTp0aKqeT5+Rh5w4AUWLmhGFp0+bH6bTIKPXrC4Z/f/ZU+dQLNeW6s/6hx/g0Ufh7FnIm9d0rOre3V3etXx52L8fSpdOfyyu4zP6vRMREclKmXH9avsIDRGvY1nwySdm+3//S3cyQ2x2qXc4DRqYpFTr1mYCv+Bgjz+VJ3pcZVpvqxEjkpIZ77zjsWSGSGqd8S3IY8UXsSL+Nti2zZTx++knMwmriEg216dPH3fp2ytdOap7//79mR+QpI5rdEbVqmlOZoik6L77YMMGeOQR8/3q0QO++86U4CxWDMh4MsKVXBEREcnt9EutyJXWrjXzDAQGml41kn0VLw4LF5re4H/8YSZ3T0jw+NMcOGB6TKXX/v2eGYJ+lZEjwTUH0ZtvwnPPZcKTiFzfUf9S5t9ivnymZnnHjpnyb1FERCRVXPNnqNyUeFKZMrBihSk/FRAA8+aZicM/+QSH5bQ7OhERkRxDCQ2RK7lGZzz8cO4tUZSTVKmSNHfEvHmmt1RiosefxtXjKj1LRoaep8iy4LXX4IUXzO3XX4eXXvLwk4ikUbVqZv6WwED4/nvTgzET/i2KiIhcl2uEhiYEF0/z9YWBA2HdOqhbFyIj4cknmXX4NqrEbrQ7OhERkRxBCQ2Ry505YyZxA+jd295YxHMaNYKZM035sM8/hw4dMl5Q11vFxZmRRa5a3K+9ZiYqFPEGt98OX38Nfn7mb+1jj4FTPRZFRCQLnT9vSgOBRmhI5qlWzYwQ//BDyJuXmrGr+e5QHdO56tgxu6MTERHJ1jSHhsjlpk6FixehRg0z94LkHPffb35I7dDBrKOj4ZtvMmVODducOQPt2sGyZaZ32Mcfm8ntryGjc38cOGB+lxZJk9atTYLx4YdNgjEgAMaP13xFIiKSNdasMWUPS5Qwk5iJZBZfX+jTBx58kHkVB9A6egZMmmSug158EQYM0JxiIiIi6aBfD0RcEhNhzBiz/cQT4HDYG494Xtu2puxUcLCp5x8RkXN6SP3xB9SubZIZoaHmdf5HMgMyPvdHfLymQZB0evBBmDbNJDE+/RSeftqUShMREclsy5aZdePGut6XrFG8OM8W/YqHSvwO9etDTAwMHgwVK8L06boGEhERSSP1rRVx+fZb2LfPzJvx6KN2RyOZ5a67YPFiuPdeWLXKJAFmzYImTeyOLH2cTjP59+DBJrtQpgx8950ZZZQKrrk/0iMgIH3HiQDQvr0pkda1qxlNFBAA772nH5dERCRzLVxo1nffbW8ckutsytPQtD9mzDBz3R06BJ07wwcfmGsglUATERFJFSU0RMD0innnHbP91FM5qwyRXO2222DtWtNLfOtWaNYM3n4b+vXLXmVv/vrLzPWycqW53b69Kd2TL5+9cYmk1iOPmKTG44/D6NFmwvDhw5XUEBGRzHHqlLkGBGjRwt5YJFs4csT0GSpfPmPncZdqdTigY0do08YkMYYPN2XQGjeG//s/0yYpW9YToYuIiORY2eiXO5FMtHKladwEBZk6p5LzlS9vyjR16mRaKQMGmAmLt2+3O7Lri442vbpq1DDf3eBgU7bnq6+UzJDsp0cPM0IDTCP+hRdUekFERDLH4sXm/5hq1cwcGiLXkZBgyqxm1FWlWvPkgZdfNkOlH3/cdKr6+muoXBmefx7Ons34k4qIiORQGqEhAkmjM7p1g0KFbA1FslBICHz5pekRNXAg/PYb1KwJgwaZhkRIiN0RJnfhAkycCCNGJM398cADZpi6JrWU7Kx3b9PKf+YZ8/f43DkYOzZ7jZgSERHv5yo31bKlvXFItuLvn/4SrS7XLNVarJi5vn/6aXjuOZN0e/ddmDwZhg6F//3PBCAiIiJu+qVA5K+/YP58M/y3f3+7o5Gs5nCYH1O3bYNWrUz5m9deM3NRvPcenD9vd4Rw5oyJpVw56NvXJDPKlIG5c2HOHCUzJGd4+mnToHc4YNw4M5eRZp0XERFPcTph0SKzrYSGeJvq1c3388cfzSiNf/8110bVqsEPP2j0qoiIyGWU0BB5912zbtMm48VRJfu66SbTWJg509St/ecfU4aqbFkz4fb+/Vkbj9NpRox07QrFi5tYjh0zcY4fD3//Dffdl7UxiWS2xx83pdP8/GDaNDMCKTra7qhERCQn+PNPOHHCjMBt3NjuaESu5nDAPffA5s3wySemcsCOHXD//RARQaXYP+2OUERExCsooSG52+7d8PnnZnvgQHtjEfs5HPDwwyZZ8NlnZuTDiRPwxhsmsdGiBUyYAIcPZ87zx8SYnllPPgk33mga259/Dhcvml5bEyaY8e69ev3HuHWRbK59e/juO1Nb+scf4c47zb9DERGRjHCVm2reXNdR4t38/OCJJ0xb9cUXITAQli5lzqHavHayt5ncXkREJBfTHBqSuw0bBomJZth5gwZ2R5MjHDliqsSkZ7DLgQPm+t12/v7w2GPQpYsp6TRxIvz8M/z0k1nAJBgaN4Y6dagUW4e9/hWBoNQ/R0yMmYD8r7948dSf1L3wK4RvSF5iJ29eaNfO1M6tX98kXERyg9atYelSMwpp/Xpo2NAkNypVsjsyERHJrjR/hmQ3YWEwfLhJbgwciO+sWXSKGgflZ5h2bO/eXtJ4EhERyVr6309yr7/+MhNCg+mBLx6RkADx8ek7Nr3HZZqAADNi4+GHYe9eUwpn/nz44w8zFHzzZgB+cO1fuJApCVWwoClnEBICvr4QG2uWc+dM2ahjx0xd3Et6XP6cJUuaoeZt20LTpqZHlkhu1KABrFplfnjas8fc/uor8+9DREQkLSIj4fffzXaLFvbGIpJWpUrBzJl0/u1JXvmnL5XP/gnPPGPK0I4eDRERdkcoIiKSpZTQkNxr6FAzuVrbtlCnjt3R5Cj+/qYyUlp59ej/smXh5ZfNcuqUGbGxbh2sX0/Uyo2EOSPNvBv//JP6cxYuDFWqMO6XW/iDBuy7sQlH/UvBUsySSqVKmXBEcpybbzZJjQcfhF9/hVatYMQIeP55jVgSEZHUW7rU9LqpUMFc04lkQ2vy3EGbkuvZ8dxEeOUV2LYN7rrLzAU5apS+2yIikmsooSG506ZNMHu2+UHstdfsjkaym4IFoUMHswB1brbI5zzDum8PwsGDcOaMKSkVE2NKmgUFmZEWoaFQtCgUK2Ym+i5QAIBnAszolJv90x5KVs9VLpLlChWCJUvg6afNPDIvvGDKUE2caEoxiIiIXM/cuWat0RmSzTkdvqYEVfv2poPe2LGmRO6CBTBgAAwaZNocIiIiOZgSGpI7DR5s1h06QLVq9sYi2Z/DQaRvAahZAGrWTNcp0juqJT1zlYhkOwEBpqxCrVomsTFrlklqzJypEXYiIvLfLlyAb74x2w89ZG8sIp6SPz988AH06gX9+pnh2m+9BVOmwNtvQ+fOGs0qIiI5lo/dAYhkucWLYd48M7fBq6/aHY2IiKTWE0/AL7+YOmt79pjJwt9/H5xOuyMTERFv9eOPZh6zkiWhUSO7oxHxrFtugZ9+gu++gzJl4OhReOQR811ft87u6ERERDKFRmhI7hIXZyZQA3jqKahY0d54xKOOHDHlkdM7akFzUYhkAw0awMaN0KOHabz3729KLUyaBOXK2R2diIh4m+nTzbpjR/BRfz7JgRwOM49Gy5bw3ntmpMaqVVCvHnTvbm4XKWJ3lCIiIh6jKzrJXT78EP7+29Rk19wZOU5CgpmLIj3274cDBzwajohklvz5TfmQTz6BkBBYuRKqVzd/4xMT7Y5ORES8xdmzMH++2e7UydZQRDJdUBC89BLs2AFduoBlmQ4f5cvDO+/AxYt2RygiIuIRGqEhucexY0lJjBEjIDzc1nAkc2guCpFcwuEwJahatDCjNZYtMyPwpkwxE2Q2aEBERMYSlRq1JSKSzX33HcTGQpUqJvEtkhuUKAFffAFPPmmujdatg4EDzZwbr7wCjz1m5icTERHJpjRCQ3KPF1809XPr1YNu3eyORkREPKFMGZN1+Phjk6jesMHMrfHYY1zcc4T9+9N3Wo3aEhHJAVzlpjp10gTJkvs0bAirV5tRGiVLmvq8vXubssvjx2vEhoiIZFtKaEjusHgxfP652f7wQ9XPFRHJSXx8TAN9xw5TKxpg8mR+PnAzY0NfYNfq0+zaRZqW0qVtfUUiIpJRx47B0qVmu2NHe2MRsYuPj7k22rXLtIOLFjW9Np54wnQKeecdiIy0O0oREZE00a+6kvOdPZv0A9dTT5kRGiIikvMULmx6Ia5aBY0bE2RdpNfZkVC2rCmxcPKk3RGKiEhWmTULnE5o0MD8PyCSmwUGQp8+sGePKT1VsiQcP25KUZUoYR77+2+7oxQREUkVzaEhOd8zz5jhtTffDG+/bXc0IiKSSumfA6MBWCu5xXc+bzgHUTVyK7z5JowaZebbeOYZqFDB0+GKiIi3sCyYONFsazJwySGOHIGEhIzO/RcMPEO5m59g4bDp8O67sG2bmX9s7Fho2tRcKz34IOTJ46HIRUREPEsjNCRn+/ZbMyGaj48pORUSYndEIiKSSgcOkO45MHA4+D6xNXV9N8E338Ctt5pa0WPHmtrRd91l/o+Ij/dgxCIi4hUWLDA/0ubNC48+anc0Ih6RkOCZy5b9+2HPoQAzr+SWLbBkCdx/v5lnZtky6NIFihWDnj3NY4mJGX9SERERD9IIDcm5jh2D//3PbL/wgpkUTUREspXSpU3Z5/QICAAnvqaXYdu2sHw5vPcezJ9vJhL/+WcoWBDat4fOnU1ZEg9MGpv+kSVJSpUy4YmISDq8+65Z9+wJ+fIle8gTf6MPHAA/taTFBv7+6b8uckk2wsPhgGbNzHLgAEydCpMnm6zHp5+apXBhcx11331mP43cEBERm+kyTHKm2Fho1w5OnYLq1eHVV+2OSERE7ORwmDIKTZuaRvqECWa+jRMnksos3HQTPPAAtGmDn9WEBId/up7KNbIkvROLp3tUioiIwPr1ppe5nx/063fVwxn9Gw0a3CfZ27VLV5UChuDwfYV6xVfQOnoGLaK/Jv/JkzB+PIwfzwVHHv7I04zfg5tzoGwzJqyqZqohiIiIZCElNCTnsSwzqdmqVRAebkqNBAbaHZWIiHiL0qXhrbdg2DBTSuHLL+G77+DgQfjwQ/jwQ9Y68rIuTxN491ISpGZN8PVN01Oktwdlxmpji4jkcq7RGe3bm4mPU5CRv9FgRgCKZFfXK11lOXxYHdyU1cFNea3QRzQ8v4TmMXNpen4exRMO0fT8fJqenw+ngIL5TVnPunXNcuutZpLxVI549cSIKdDIVhGR3EYJDcl5xo83Q2MdDvjqKzMZuIiIyJX8/KBFC7OcP29awnPmwA8/EHrqFHee/xGe/9HsGx4Ot98Od9xhGuw1a0JYmI3Bi4jIVfbvh9mzzfZzz9kaiog3S33pKn+gpVmssbB5MyxezPJXl3LrhZWEnDkDP/1kFpciRaBOHahUybTFy5c365Ilr+oc4okRUxrZKiKS+yihITnL0qXwzDNme/hwaNkyQ6fLaI8R1dcVEckmgoPNhJj33w+JiTxQZjP1zy/jpQZLYeVKOHsW5s41i0uFClC7dtJyyy2mEU/G5+EQEZF0GD3aTGAcEWESzyLiOQ4H1KgBNWpw75DncPjGcVfRLVSLXUe12LVUu7iO8nFb8TtxAn780SyXicOfQ/5lOeZ3Eyf9ivGPbzHuiyrGCb/iTJtcDIoWNddRoaFpmtNMI1tFRHIf/dQqOcfvv5sfouLjzRDzgQMzfMqM9hhRfV0RkWzI15e/AmvxV2AtXprX39Rm2LDB1GRftcpsHzoEO3eaZcaMpGPDw5l1oRJ7AyrByMpQubJJfJQpoxolIiKZadcu+OQTs63RGSKZKiEB4hMC2BZUh21BdZjB/wAIcp6nUuyfVInbSKm43ZSO30Wp+N2UjN9LAHGUi99BufgdyU8WBzS57HaePCaxUbiwWV++XHlf/vyoI4mISO6jhIbkDBs2wL33QkwM3H03TJ2apl4d/yUjNXb125VktmtP6nd9GkEkkkp+flCvnllc/vnH/N/jWjZuhH374OxZavEHtWL/gBcuO4ePj/kPxVV6wbXcfLNJdvgnTUCekX/XoDrSIpILuebQi4szZQTvvtvuiERyvJTLVgUDDS8tl0lMhMOHYfdusz52DI4d4+sPj1GUYzQucxSOHzclQC9cML0KU1NLKjSUeXGlOexXBvqWMddaZcokXWepQS4ikiPppyzJ/jZvNo2WyEho0sRM7KpJwCWXuN6kfv9FI4hEMqBQoaT5N1wuXoRdu3j67r+5OW47fVv8Ddu3m9Z+TAzs3WuWy+tMg6knXbq0u/H9v9ib+dtZnri48hzxL02iI/WXa6ojLSK50jffmL+tAQHw4Yce69gkIh7i62t6XJQqlezuTpcGVcXtuXRHTAycOJHycvJk8tuRkRAdTUW2UjFuK4xJ4TnLlYMqVcyI2cqVTXnQW27R7wUiItmcEhqSvS1ZAg8+CFFRcOutMG+eqYMukoukflK/5NRhScTDgoKgWjUWhlYDoO/0S/dblul1uGtX0rJ7d9L6/HnYs8csCxfyvut8BzGjQy5LdiQb2VGq1FXDrIKDzegrje4QkVzj3Dno189sv/iiCuqLZGchIVC2rFmu58IFOHiQx5ruo2TCPl7rvt+Mlt23D3bsMH8bXOVB58xJOs7PzyQ3atZMWmrUgBtuyJSXJCIinqeEhmRfU6fC44+bLuq3324uUsLC7I5KREQkOYcDihUzy+23J3/MsuDo0WRJjjnv7OJmaxdVg3abUR+7d5tlwYLkx/r7J5VVqFwZqlenSnx1tiRUAtLe81CjO0QkWxo2zNTqK1PGJDREJHfIkwcqVmTG2YokJMD0by97rLBFkRuOcnPcX5SL2065uO3cHPcXFeK2EJ5wBrZsMcsXX7gPOepXkv3hNbmtd02oUwfq1oXixTXiS0TECymhIdlPXBwMHgwjR5rbHTvC5MkaNioZorkoRMQWDgeUKGGWO+8E4OFLQzTiYpxJyY4rR3fs3g2xsUk9D+fPB2AdEI8f/kGVoHp1s9SoYdbFiv1no1ydmkUk2/nhBxg1ymx/9JH5gVNEcpUUS/A6HJzwK8EJvxL8FnxX0v2WRdGEw1SJ20Tl2KTlpoS9FE84RPFTh+D1H5L2L1rUJDbq1k1KchQtmiWvS0RErk0/wUn2smsXdOoE69aZ2y++CG++aSZbFckAzUUhIl7HxwduvNEsTZsmf8zpNJNqupIc27bB5s2cWbmZ/JyFrVvNMn160jE33AC1akHt2mapVcuUrtL/oSKSHW3ebNoFlgX/+x/ce6/dEYmITVJfgtcBlLy03Jd0d2QkEYU3UzVhE01CNnBL7HrKx23D9/hxU9Z63jz3rsd9S7AlqC5bA+uyJbAu24LqcNq3ULJnUQlPEZHMpYSGZA+JifDppzBggJkoLH9+mDgR2rWzOzLJQTQXhYhkGz4+cNNNZmne3H13EX+LGznM3jmbzY99rmXHDvj3X9O6vryFHRpqEhu1atE2qjbbAmtDQmUNOxMR73biBNx3H0RHQ7NmZiJwEZH0ypePlVYTljibML+IuSvIeZ5KsX9SLXYdVWPXUS12HeXitlM08QhFY45wV8z37sMP+5Vic1A9/gysz6Kz9TnhrA1obk8Rkcyi1qp4v99+g6efho0bze077zS1Lm+80dawREREvI7DwWFKQquS0KpV0v0XL5pRHBs3woYNZvnzT/Nj4C+/wC+/MNK1b2igKVHlGsVRuzZUq2YmPRcRsduZM/DAA3DwoKmVN3u26ZUiIpJByTu4BQMNLy2XREeba6n1603ViHXrYMcObkw4wI3RB7g3ejaDgIS9vlCrGtSvD/XqmXWlSuDrm+WvSUQkJ1JCQ7zXpk3w1lumkQKQLx+89hr06aMLARERkbQICjK1n+vUSbovIcGM3LiU4Fg9biNV4jaSNzYK1q41i4uvL9xyS/KSVTVqQN68Wf9aRCT32rULWrc2cweFh5syMAUK2B2ViOQWoaHQpIlZXCIjTYJjzRpYvZpjc1ZTjGPm94xNm2D8eACiHXnZElSXPwPr82eQWf7xK3bNp1LZKhGRa1NCQ7yL0wkrVsA778CCBeY+hwMef9zMlVGo0H8fLyIiXuXIEfO7eXomnD5wQJWPMpWfn0lS3HILPPIIXeaBw3Kyc+He5CM5NmyAU6eSyldNnWqOdzjMB3v5nBy1apm5OkREPG3pUvi//zMjNEqWhLlzoUIFu6MSkdwuXz5T+q5ZMwBK+VsUTjhCm6KrqXHRLFVj1xFqnaPhhWU0vLDMfegxvxvdCY7NQfXYGliH8z6h7N9v02tJQUSEuSb3BCVpRMRT9DOBeId9+0wZqalTYe9ec5+PDzz8MAwaZEpfiIhItpOQAPHx6Ts2vcdJ+lkOHzNR+M03w0MPXbrTMpkpV3LDlew4fNj0kt65E2bMSDpJqVJJIzluucWUWLj5Zk04JCLp888/8Oqrppez02lKt8yZA0WL2h2ZiMjVHA5O+t/IR8duBC7N+ZmQAH/9BatXu0dysG0bxRIOUyzhMC1jvjH7+fhA1arMDK7Hn5H1YVNdqFgR8uSx7eUcOAD790Pp0hk7jzclaUQk+/OKhMbYsWN55513OH78ODVq1ODDDz+kXr1619x/9uzZDB48mP3791O+fHnefvtt7r33XvfjlmXx6quvMnHiRM6ePUujRo345JNPKH9Z99DTp0/z9NNP88MPP+Dj40O7du344IMPCA0Nde+zefNmnnrqKdauXUuhQoV4+umnGThwYOa8CblNbKwpZfHjj2ao+JYtSY/lzQudO8Nzz0G5cvbFKCIiHpG8HnHq6ffvrHXt0TQO4MZLy/3mriAoUOYfqsRupErsBm6J3UCV2I2Ujt9tWr4HDpgfHF18faFsWahc2SQ4ypc3t8uUMT2tNRRHsilPt2PkMlFRMHEivP66KekC0LUrjBunOX1EJHvx8zOdNKtXh549zX3R0WYODleCY/VqczG2eTPt2Ux7PoVamBGxpUqZ6yfXUrGimVO0WDEICcn08EuXTuFa3rLMHG0xMXD+vFlfvAiJiSb5nJiYtDiddO2SiE9MIixwmuMdDpPAcTiSlrTeTu0xrngSEiAhgT69E/nnWAK+JOJrJeBHAj5WonvtSyKJ+JLo8CMBv2TrWEcQF3xCiHGEcsEnhEKlQ1iwJMA8l4hkGdtbjzNnzqR///6MGzeO+vXrM3r0aFq0aMGOHTsoXLjwVfv//vvvdOzYkeHDh9O6dWumT59OmzZt2LBhA1WrVgVg5MiRjBkzhqlTp1KmTBkGDx5MixYt+Ouvvwi6dPHbuXNnjh07xuLFi4mPj6d79+706tWL6dOnAxAVFcXdd99NREQE48aNY8uWLTz22GOEh4fTq1evrHuDcoKEBNN7c/NmU0Pyt99MMiM2NmkfHx9o2hS6dYMHH4TgYLuiFRERyZXSOprmtG8hfg2+m1+D7wZMz7uqZSLZOHmTGcWxcSNs3w5//w3nzpmW8K5dpkzM5Xx9TUO9bFnTYi5RImm58UazLlBADUXxOpnRjsn14uJg5Uozavubb+DCBXN/zZrw/vtw5512Rici4jmhoeZv2uV/144cgTVrGP/YamrErqZB0J+mzN7+/WZZuPDq84SFmcSGawkPN0mO4GCzdm0HBJgf9q9cEhKSJyVcy6XbXxyJIY8zBqqeT77f+fMmqZFKU10bXpDD/8iTJ9sHCT6+xPjk5ZxPvktLuFn75nPfF31pHeV6zCcf0ZceDy+Vj3nLQnStmwlUMi3nclhWGv4CZYL69etz66238tFH5k+K0+mkZMmSPP3007z44otX7d++fXtiYmKYN2+e+74GDRpQs2ZNxo0bh2VZFC9enAEDBvDcc88BEBkZSZEiRZgyZQodOnRg+/btVKlShbVr11K3bl0AFi5cyL333svhw4cpXrw4n3zyCS+//DLHjx8n4FIX0RdffJE5c+bw999/p+q1RUVFkS9fPiIjIwkLC8vQ++S14uLMf7CnT8Px43DwYFLPzMu34+KuPrZgQfPXpXVraNnSK2tuu3qppqdnMST1Lk7p5XvrsXY+t+LOPsfa+dzZNW47n1txp/3Y4GDTvitVKu3Huub+OH8+7cdC9n2/r/l/pmXBsWMmseFKcOzZY0pM7tuXuicMDDTXDQULmuuFK7dd6/Bw8wPB5UuAes2lVq64dvUgT7djUiNHfUYXLpi/BTt3wp9/wi+/wB9/JCUxwPRCHjjQjMzw9fXI02b0+h4y/vfSk+dRLN4fi6fOo1i8PxZPncd9HXqTRYHEfygbv4OycX9TNv5vysX9Tan4XRRJOEKwlc6LTU8LCDCJk8BAcxHs42P+ZrsWHx+2bvclAV/8A30AcFgWDiwcOC+tLy1WCvdh4WM5SUgw2/5+Sfdx5X44cViW+34fnDjxIdHh5x51EZtg1jdX8DXx+l6xdo3quDSiwz26Iz7e/B/lSvpk9MtyOV9fMx/LtZbw8OS38+Y173dAAAQG0qtPAAePBxDvCCAOs758ScAvVdfDnvjRPiNJBIflxI8E/Kx4ytwYz/dfxye9967lytsp3Xfp9osD4ok8FU/hGxLwt+Lxu7T4E4+vlYAPzkvfQh/zjXH4JLvtdPhg4cPxM4GE3BDE6+8EmVGiriUwMPntPHmSLxqFDmTO9aut72xcXBzr169n0KBB7vt8fHyIiIhg1apVKR6zatUq+vfvn+y+Fi1aMOdSWYN9+/Zx/PhxIiIi3I/ny5eP+vXrs2rVKjp06MCqVasIDw93JzMAIiIi8PHxYfXq1bRt25ZVq1Zx++23u5MZrud5++23OXPmDPnz5/fEW5A5TpyAJUuS/uhePtTvWsuV+yUkmOGC58+bP9iu5fx5M+T7zBmzpPbXmtBQqFbNLA0aQKNGpjWhHxhERMQLae4PD3I4oHhxs1yaMNPN6YSjR01iY+9e0/o5csTMz3HkiFlOnTKjOl2308rPzzT6XAmO4OBkDUACAlJeLn/M1bh1lTFwbadncV37XLm+8r4774RChdL+eiVLZEY7xuv9+qv5N3h5KRHX9pXlRVztiMt78sbEmPbDiRNmOXUq5d69BQuaOXy6doV69dReEJFcx30d6nBw2q8wp/0Ksy5Pk+Q7WRah1jkKJRyjcMJRCieadYjzHHmsGPI4z5N47jyFgmO4547z5sf3S8mFZIuvb/LRHJeP6ggJoe9LIVzwCWHCtEv3ufZ1rVP5o21tV6LnYvrfl4AA877cXDr95wDYvRv8/SAudX2VrynYP54QYvhnX7QZjRwZmfJy9uw1H0s8fRZfLv0fevq0WdJhwnUed+IwyQ0CiLsy2eHwJxFfLsb54nvIF+pfluC51mJZV///f2l7xJpEEi4mkifQiQ+J+FqJ+JCIvxWP76VkxeVJBde2nxVv3guXvcDVA17TZIRr45+Mncd9jm5pPMbPLym5kVLC41qLv3/y9/vKzyOlz+fyUmuQ8u0CBaB5cw+8GfazNaFx6tQpEhMTKVKkSLL7ixQpcs1REMePH09x/+PHj7sfd933X/tcOQzcz8+PAgUKJNunTJkyV53D9VhKCY3Y2FhiLyujFHmp1mtUVFSKryXTrF9v5qDISvnymcbHTTeZ8hAlSyYtN91k1j4+yY85dy5rY0wHp9P8tpLeqTxcP2ql53i7jrXzuRV39jnWzufOrnHb+dyKO/3HOp3/vd9/HZ/b3u8DB0yvrjRf9oSFQY0aZklJbKwZBXr6NPz7r1nOnEnavvz+c+dMTeqYmKTSlgkJSR0xspMffzQdQLKQ65rV5gHc2UJmtGNS4jXtC4A33oBFizx7zrAw08np5ptNp6fbboMKFZLaDZnQXsjo9T1k/O+lJ8+jWLw/Fk+dR7F4fyyeOk9qr0OjgCi/YuzxK5bi4wdiwBELN2Xgh/uD/5rfrpc8ntKjFpC6Tq5Z+b6k9lwZ/awvJMAFfCh3RxgQBpRI/cEOIBwOnLUIdcRQqUQUeZ1RhCRGEeqMJNRpbud1RhJime1QZyShiVGEOqMIsaLxt+Lws+Lwt+LxSYwjgDgCMesAruxhZYEVC8TiZ5kfg1Ocaj4Wotak6+1wq3DZuVLDeWnXa+2eiIME/InHnwSHP06Hr3vblYxxbSfgR7zDH6fDjwT8OXfRj3j8CQj2J9HhR/yl+137mw/CjOTBujTCxzVGw3JeGuWTSHx0HPmDL9KsYaxpY1y8mHx94YJJGrrWLgkJ5lrGW37/rF0bli3L8qfNjDaGxr540PDhw3nttdeuur9kyZI2RJPFXBnmPXvsjiRT7N1r3/F2HWvncyvu7HOsnc+dXeO287kVd/Y51s7nzuix+fKl/3i5jI0TRZ87d458+iC9Qo5vX0RFmc5Y69fDzJlZ+tQZ/RvtqXN46jyKJfPO4W3nUSyZdw5vO49iybxzeNN5IoHVBz0SSg5lAXFmsS7dTCtPVGc7DyzxwHnstGGDrY01T7YxbE1oFCxYEF9fX06cOJHs/hMnTlC0aNEUjylatOh/7u9anzhxgmLFiiXbp2bNmu59Tp48mewcCQkJnD59Otl5Unqey5/jSoMGDUo2jNzpdHL69GluuOEGHLlwqHRUVBQlS5bk0KFD2b/Gr2SIvgsC+h6Ioe+BgL4H3sqyLM6dO0fx4sXtDsXrZUY7JiVqX3ie/v7YT5+Bd9DnYD99Bt5Bn4N30Odgv8z6DDKjjWFrQiMgIIA6deqwZMkS2rRpA5iL9CVLltCnT58Uj2nYsCFLliyhX79+7vsWL15Mw4YNAShTpgxFixZlyZIl7gRGVFQUq1evpnfv3u5znD17lvXr11OnTh0Ali5ditPppH79+u59Xn75ZeLj4/H393c/T8WKFa85f0ZgYCCBgYHJ7gsPD0/z+5LThIWF6Y+RAPouiKHvgYC+B2Loe+B9NDIjdTKjHZMStS8yj/7+2E+fgXfQ52A/fQbeQZ+Dd9DnYL/M+Aw83cbwuf4umat///5MnDiRqVOnsn37dnr37k1MTAzdu3cH4NFHH0022V7fvn1ZuHAho0aN4u+//2bo0KGsW7fO3XBwOBz069ePN954g7lz57JlyxYeffRRihcv7m5sVK5cmZYtW9KzZ0/WrFnDb7/9Rp8+fejQoYM7W9SpUycCAgLo0aMH27ZtY+bMmXzwwQdXTeQnIiIiIiK5j6fbMSIiIiIicn22z6HRvn17/vnnH4YMGcLx48epWbMmCxcudE+Yd/DgQXwum0z6tttuY/r06bzyyiu89NJLlC9fnjlz5lC1alX3PgMHDiQmJoZevXpx9uxZGjduzMKFCwkKCnLvM23aNPr06UPz5s3x8fGhXbt2jBkzxv14vnz5+Omnn3jqqaeoU6cOBQsWZMiQIfTq1SsL3hUREREREfFmmdGOERERERGR/2Z7QgOgT58+1+yZtHz58qvue+ihh3jooYeueT6Hw8GwYcMYNmzYNfcpUKAA06dP/8+4qlevzi+//PKf+8i1BQYG8uqrr141TF5yH30XBPQ9EEPfAwF9DyTn8HQ7RjKf/v7YT5+Bd9DnYD99Bt5Bn4N30Odgv+z0GTgsy0rP/PAiIiIiIiIiIiIiIiJZxvY5NERERERERERERERERK5HCQ0REREREREREREREfF6SmiIiIiIiIiIiIiIiIjXU0JDMs3YsWMpXbo0QUFB1K9fnzVr1tgdkmTAypUrue+++yhevDgOh4M5c+Yke9yyLIYMGUKxYsXIkycPERER7Nq1K9k+p0+fpnPnzoSFhREeHk6PHj2Ijo5Ots/mzZtp0qQJQUFBlCxZkpEjR2b2S5NUGj58OLfeeit58+alcOHCtGnThh07diTb5+LFizz11FPccMMNhIaG0q5dO06cOJFsn4MHD9KqVSuCg4MpXLgwzz//PAkJCcn2Wb58ObVr1yYwMJCbb76ZKVOmZPbLkzT45JNPqF69OmFhYYSFhdGwYUMWLFjgflzfg9xnxIgROBwO+vXr575P3wMR8TZqn9jreu0JyXypuZ6XzHe9a2nJeildy0rmGzp0KA6HI9lSqVIlu8PKlY4cOUKXLl244YYbyJMnD9WqVWPdunV2h3VNSmhIppg5cyb9+/fn1VdfZcOGDdSoUYMWLVpw8uRJu0OTdIqJiaFGjRqMHTs2xcdHjhzJmDFjGDduHKtXryYkJIQWLVpw8eJF9z6dO3dm27ZtLF68mHnz5rFy5Up69erlfjwqKoq7776bUqVKsX79et555x2GDh3KhAkTMv31yfWtWLGCp556ij/++IPFixcTHx/P3XffTUxMjHufZ599lh9++IHZs2ezYsUKjh49yoMPPuh+PDExkVatWhEXF8fvv//O1KlTmTJlCkOGDHHvs2/fPlq1akXTpk3ZtGkT/fr14/HHH2fRokVZ+nrl2m688UZGjBjB+vXrWbduHc2aNeOBBx5g27ZtgL4Huc3atWsZP3481atXT3a/vgci4k3UPrHf9doTkvlScz0vme9619KSta51LStZ45ZbbuHYsWPu5ddff7U7pFznzJkzNGrUCH9/fxYsWMBff/3FqFGjyJ8/v92hXZslkgnq1atnPfXUU+7biYmJVvHixa3hw4fbGJV4CmB999137ttOp9MqWrSo9c4777jvO3v2rBUYGGh99dVXlmVZ1l9//WUB1tq1a937LFiwwHI4HNaRI0csy7Ksjz/+2MqfP78VGxvr3ueFF16wKlasmMmvSNLj5MmTFmCtWLHCsizzmfv7+1uzZ89277N9+3YLsFatWmVZlmX9+OOPlo+Pj3X8+HH3Pp988okVFhbm/twHDhxo3XLLLcmeq3379laLFi0y+yVJBuTPn9/69NNP9T3IZc6dO2eVL1/eWrx4sXXHHXdYffv2tSxLfw9ExPuofeJdrmxPiD2uvJ4X+7iupSVrXetaVrLGq6++atWoUcPuMHK9F154wWrcuLHdYaSJRmiIx8XFxbF+/XoiIiLc9/n4+BAREcGqVatsjEwyy759+zh+/HiyzzxfvnzUr1/f/ZmvWrWK8PBw6tat694nIiICHx8fVq9e7d7n9ttvJyAgwL1PixYt2LFjB2fOnMmiVyOpFRkZCUCBAgUAWL9+PfHx8cm+B5UqVeKmm25K9j2oVq0aRYoUce/TokULoqKi3D2SVq1alewcrn3098M7JSYmMmPGDGJiYmjYsKG+B7nMU089RatWra76rPQ9EBFvovaJSMquvJ6XrHfltbRkrWtdy0rW2bVrF8WLF6ds2bJ07tyZgwcP2h1SrjN37lzq1q3LQw89ROHChalVqxYTJ060O6z/5Gd3AJLznDp1isTExGQ/UAAUKVKEv//+26aoJDMdP34cIMXP3PXY8ePHKVy4cLLH/fz8KFCgQLJ9ypQpc9U5XI959XC3XMbpdNKvXz8aNWpE1apVAfMZBQQEEB4enmzfK78HKX1PXI/91z5RUVFcuHCBPHnyZMZLkjTasmULDRs25OLFi4SGhvLdd99RpUoVNm3apO9BLjFjxgw2bNjA2rVrr3pMfw9ExJuofSJytZSu5yXrXOtaWrLOf13LStaoX78+U6ZMoWLFihw7dozXXnuNJk2asHXrVvLmzWt3eLnG3r17+eSTT+jfvz8vvfQSa9eu5ZlnniEgIICuXbvaHV6KlNAQEZE0e+qpp9i6davqW+ZiFStWZNOmTURGRvL111/TtWtXVqxYYXdYkkUOHTpE3759Wbx4MUFBQXaHIyIiImmk63l7XetaWkmNrKFrWe9wzz33uLerV69O/fr1KVWqFLNmzaJHjx42Rpa7OJ1O6taty1tvvQVArVq12Lp1K+PGjfPahIZKTonHFSxYEF9fX06cOJHs/hMnTlC0aFGbopLM5Ppc/+szL1q06FWTLiYkJHD69Olk+6R0jsufQ+zXp08f5s2bx7Jly7jxxhvd9xctWpS4uDjOnj2bbP8rvwfX+4yvtU9YWJh6Y3uRgIAAbr75ZurUqcPw4cOpUaMGH3zwgb4HucT69es5efIktWvXxs/PDz8/P1asWMGYMWPw8/OjSJEi+h6IiNdQ+0QkuWtdz0vWuda1tGSN613LJiYm2h1irhQeHk6FChXYvXu33aHkKsWKFbsqmVq5cmWvLv+lhIZ4XEBAAHXq1GHJkiXu+5xOJ0uWLFFNyByqTJkyFC1aNNlnHhUVxerVq92fecOGDTl79izr169377N06VKcTif169d377Ny5Uri4+Pd+yxevJiKFSuq3JQXsCyLPn368N1337F06dKryoPVqVMHf3//ZN+DHTt2cPDgwWTfgy1btiRLbi1evJiwsDD3f6ANGzZMdg7XPvr74d2cTiexsbH6HuQSzZs3Z8uWLWzatMm91K1bl86dO7u39T0QEW+h9omIcb3rebGP61passb1rmV9fX3tDjFXio6OZs+ePRQrVszuUHKVRo0asWPHjmT37dy5k1KlStkUUSrYPSu55EwzZsywAgMDrSlTplh//fWX1atXLys8PNw6fvy43aFJOp07d87auHGjtXHjRguw3nvvPWvjxo3WgQMHLMuyrBEjRljh4eHW999/b23evNl64IEHrDJlylgXLlxwn6Nly5ZWrVq1rNWrV1u//vqrVb58eatjx47ux8+ePWsVKVLEeuSRR6ytW7daM2bMsIKDg63x48dn+euVq/Xu3dvKly+ftXz5cuvYsWPu5fz58+59nnjiCeumm26yli5daq1bt85q2LCh1bBhQ/fjCQkJVtWqVa27777b2rRpk7Vw4UKrUKFC1qBBg9z77N271woODraef/55a/v27dbYsWMtX19fa+HChVn6euXaXnzxRWvFihXWvn37rM2bN1svvvii5XA4rJ9++smyLH0Pcqs77rjD6tu3r/u2vgci4k3UPrHf9doTkvlScz0vme9619JijyuvZSXzDRgwwFq+fLm1b98+67fffrMiIiKsggULWidPnrQ7tFxlzZo1lp+fn/Xmm29au3btsqZNm2YFBwdbX375pd2hXZMSGpJpPvzwQ+umm26yAgICrHr16ll//PGH3SFJBixbtswCrlq6du1qWZZlOZ1Oa/DgwVaRIkWswMBAq3nz5taOHTuSnePff/+1OnbsaIWGhlphYWFW9+7drXPnziXb588//7QaN25sBQYGWiVKlLBGjBiRVS9RriOlzx+wJk+e7N7nwoUL1pNPPmnlz5/fCg4Ottq2bWsdO3Ys2Xn2799v3XPPPVaePHmsggULWgMGDLDi4+OT7bNs2TKrZs2aVkBAgFW2bNlkzyH2e+yxx6xSpUpZAQEBVqFChazmzZsna4Dpe5A7XdkI1PdARLyN2if2ul57QjJfaq7nJfNd71pa7KGERtZr3769VaxYMSsgIMAqUaKE1b59e2v37t12h5Ur/fDDD1bVqlWtwMBAq1KlStaECRPsDuk/OSzLsrJuPIiIiIiIiIiIiIiIiEjaaQ4NERERERERERERERHxekpoiIiIiIiIiIiIiIiI11NCQ0REREREREREREREvJ4SGiIiIiIiIiIiIiIi4vWU0BAREREREREREREREa+nhIaIiIiIiIiIiIiIiHg9JTRERERERERERERERMTrKaEhIiIiIiIiIiIiIiJeTwkNERHJUkOHDqVmzZrXvO2p84qIiIiIiACULl2a0aNH2x2GiIjXWblyJffddx/FixfH4XAwZ86cNJ9j0aJFNGjQgLx581KoUCHatWvH/v37PR6rixIaIiKSIatWrcLX15dWrVql6/jnnnuOJUuWXHe/b775hjvvvJN8+fIRGhpK9erVGTZsGKdPn07X84qIiIiIiPe77777aNmyZYqP/fLLLzgcDjZv3pzFUYmI5AwxMTHUqFGDsWPHpuv4ffv28cADD9CsWTM2bdrEokWLOHXqFA8++KCHI02ihIaIiGTIZ599xtNPP83KlSs5evRomo8PDQ3lhhtu+M99Xn75Zdq3b8+tt97KggUL2Lp1K6NGjeLPP//kiy++SG/oIiIiIiLi5Xr06MHixYs5fPjwVY9NnjyZunXrUr16dRsiExHJ/u655x7eeOMN2rZtm+LjsbGxPPfcc5QoUYKQkBDq16/P8uXL3Y+vX7+exMRE3njjDcqVK0ft2rV57rnn2LRpE/Hx8ZkSsxIaIiKSbtHR0cycOZPevXvTqlUrpkyZctU+I0aMoEiRIuTNm5cePXpw8eLFZI9fr1TUmjVreOuttxg1ahTvvPMOt912G6VLl+auu+7im2++oWvXrike53Q6GTZsGDfeeCOBgYHUrFmThQsXuh+Pi4ujT58+FCtWjKCgIEqVKsXw4cPdj589e5bHH3+cQoUKERYWRrNmzfjzzz/T9gaJiIiIiEiGtG7dmkKFCl3V1oiOjmb27Nn06NGDb775hltuuYXAwEBKly7NqFGjrnm+/fv343A42LRpk/u+s2fP4nA43D/SLV++HIfDwaJFi6hVqxZ58uShWbNmnDx5kgULFlC5cmXCwsLo1KkT58+fd5/H6XQyfPhwypQpQ548eahRowZff/21J98OEZEs1adPH1atWsWMGTPYvHkzDz30EC1btmTXrl0A1KlTBx8fHyZPnkxiYiKRkZF88cUXRERE4O/vnykxKaEhIiLpNmvWLCpVqkTFihXp0qULkyZNwrKsZI8PHTqUt956i3Xr1lGsWDE+/vjjND3HtGnTCA0N5cknn0zx8fDw8BTv/+CDDxg1ahTvvvsumzdvpkWLFtx///3u/3THjBnD3LlzmTVrFjt27GDatGmULl3affxDDz3kbrCsX7+e2rVr07x5c5W4EhERERHJQn5+fjz66KNMmTIlWVtj9uzZJCYmUrlyZR5++GE6dOjAli1bGDp0KIMHD06xs1VaDR06lI8++ojff/+dQ4cO8fDDDzN69GimT5/O/Pnz+emnn/jwww/d+w8fPpzPP/+ccePGsW3bNp599lm6dOnCihUrMhyLiEhWO3jwIJMnT2b27Nk0adKEcuXK8dxzz9G4cWMmT54MQJkyZfjpp5946aWXCAwMJDw8nMOHDzNr1qxMi0sJDRERSbfPPvuMLl26ANCyZUsiIyOTXayPHj2aHj160KNHDypWrMgbb7xBlSpV0vQcu3btomzZsmnO7L/77ru88MILdOjQgYoVK/L2229Ts2ZN92SABw8epHz58jRu3JhSpUrRuHFjOnbsCMCvv/7KmjVrmD17NnXr1qV8+fK8++67hIeHq4eViIiIiEgWe+yxx9izZ0+ytsbkyZNp164dEyZMoHnz5gwePJgKFSrQrVs3+vTpwzvvvJPh533jjTdo1KgRtWrVokePHqxYsYJPPvmEWrVq0aRJE/7v//6PZcuWAaYsy1tvvcWkSZNo0aIFZcuWpVu3bnTp0oXx48dnOBYRkay2ZcsWEhMTqVChAqGhoe5lxYoV7NmzB4Djx4/Ts2dPunbtytq1a1mxYgUBAQH83//9X7IktCf5ZcpZRUQkx9uxYwdr1qzhu+++A0zPqfbt2/PZZ59x5513ArB9+3aeeOKJZMc1bNjQfdGfGun5DzAqKoqjR4/SqFGjZPc3atTIXTaqW7du3HXXXVSsWJGWLVvSunVr7r77bgD+/PNPoqOjr5rb48KFC+7/tEVEREREJGtUqlSJ2267jUmTJnHnnXeye/dufvnlF4YNG0b//v154IEHku3fqFEjRo8eTWJiIr6+vul+3svn5ihSpAjBwcGULVs22X1r1qwBYPfu3Zw/f5677ror2Tni4uKoVatWumMQEbFLdHQ0vr6+rF+//qq/paGhoQCMHTuWfPnyMXLkSPdjX375JSVLlmT16tU0aNDA43EpoSEiIuny2WefkZCQQPHixd33WZZFYGAgH330Efny5fPI81SoUIFff/2V+Ph4j9ZfrF27Nvv27WPBggX8/PPPPPzww0RERPD1118THR1NsWLFkk105XKtElciIiIiIpJ5evTowdNPP83YsWOZPHky5cqV44477kjzeXx8TLGSyztOXWvi2svbHw6H46r2iMPhwOl0AuaHP4D58+dTokSJZPsFBgamOU4REbvVqlWLxMRETp48SZMmTVLc5/z58+6/qy6u5Ifr76OnqeSUiIikWUJCAp9//jmjRo1i06ZN7uXPP/+kePHifPXVVwBUrlyZ1atXJzv2jz/+SNNzderUiejo6GvOvXH27Nmr7gsLC6N48eL89ttvye7/7bffkpW8CgsLo3379kycOJGZM2fyzTffcPr0aWrXrs3x48fx8/Pj5ptvTrYULFgwTfGLiIiIiEjGPfzww/j4+DB9+nQ+//xzHnvsMRwOB5UrV07xur9ChQopjs4oVKgQAMeOHXPfd/kE4elVpUoVAgMDOXjw4FVtiJIlS2b4/CIimSE6Otr9mw7Avn372LRpEwcPHqRChQp07tyZRx99lG+//ZZ9+/axZs0ahg8fzvz58wFo1aoVa9euZdiwYezatYsNGzbQvXt3SpUqlWmj0zRCQ0RE0mzevHmcOXOGHj16XDUSo127dnz22Wc88cQT9O3bl27dulG3bl0aNWrEtGnT2LZtW7Jh2tdTv359Bg4cyIABAzhy5Aht27alePHi7N69m3HjxtG4cWP69u171XHPP/88r776KuXKlaNmzZpMnjyZTZs2MW3aNADee+89ihUrRq1atfDx8WH27NkULVqU8PBwIiIiaNiwIW3atGHkyJFUqFCBo0ePMn/+fNq2bUvdunUz9gZuvtxwAAAC5klEQVSKiIiIiEiahIaG0r59ewYNGkRUVBTdunUDYMCAAdx66628/vrrtG/fnlWrVvHRRx9ds0NUnjx5aNCgASNGjKBMmTKcPHmSV155JcPx5c2bl+eee45nn30Wp9NJ48aNiYyM5LfffiMsLIyuXbtm+DlERDxt3bp1NG3a1H27f//+AHTt2pUpU6YwefJk3njjDfdvMgULFqRBgwa0bt0agGbNmjF9+nRGjhzJyJEjCQ4OpmHDhixcuJA8efJkSsxKaIiISJp99tlnREREpFhWql27dowcOZLNmzfTvn179uzZw8CBA7l48SLt2rWjd+/eLFq0KE3P9/bbb1OnTh3Gjh3LuHHjcDqdlCtXjv/7v/+7ZsPgmWeeITIykgEDBnDy5EmqVKnC3LlzKV++PGAaHCNHjmTXrl34+vpy66238uOPP7qHSv7444+8/PLLdO/enX/++YeiRYty++23U6RIkTS+WyIiIiIi4gk9evTgs88+495773WXvq1duzazZs1iyJAhvP766xQrVoxhw4a5Ex4pmTRpEj169KBOnTpUrFiRkSNHuufTy4jXX3+dQoUKMXz4cPbu3Ut4eDi1a9fmpZdeyvC5RUQyw5133vmfc5f6+/vz2muv8dprr11znw4dOtChQ4fMCC9FDiuzphsXERFJhUGDBvHLL7/w66+/2h2KiIiIiIiIiIh4Mc2hISIitrAsiz179rBkyRJuueUWu8MREREREREREREvp4SGiIjYIjIykipVqhAQEKAh2CIiIiIiIiIicl0qOSUiIiIiIiIiIiIiIl5PIzRERERERERERERERMTrKaEhIiIiIiIiIiIiIiJeTwkNERERERERERERERHxekpoiIiIiIiIiIiIiIiI11NCQ0REREREREREREREvJ4SGiIiIiIiIiIiIiIi4vWU0BAREREREREREREREa+nhIaIiIiIiIiIiIiIiHg9JTRERERERERERERERMTr/T/Opp39uWRsZAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_correlation_heatmap(adro)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        },
+        "id": "ZwYvcR61nx5P",
+        "outputId": "2a44b024-d9df-42b1-d2d3-970015a44147"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DSSA"
+      ],
+      "metadata": {
+        "id": "PTWSX4dM5fsn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_dssa = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Dian%20Swastatika%20Sentosa%20Tbk_2020-2024.csv\"\n",
+        "dssa = pd.read_csv(url_dssa)\n",
+        "dssa.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "opJl-3DOFICJ",
+        "outputId": "98f0c7b7-687b-46bb-8736-63e883ea4320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close  Adj Close  Volume\n",
+              "0  2020-01-02  1360.0  1435.0  1360.0  1435.0     1435.0    5000\n",
+              "1  2020-01-03  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "2  2020-01-06  1437.5  1437.5  1435.0  1435.0     1435.0    3000\n",
+              "3  2020-01-07  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "4  2020-01-08  1435.0  1435.0  1435.0  1435.0     1435.0       0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bb697566-c021-4fea-8f46-37b4ffb0fbfb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>3000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bb697566-c021-4fea-8f46-37b4ffb0fbfb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bb697566-c021-4fea-8f46-37b4ffb0fbfb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-fefc2ec2-0c92-45da-9d83-164fd75dc4de\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fefc2ec2-0c92-45da-9d83-164fd75dc4de')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-fefc2ec2-0c92-45da-9d83-164fd75dc4de button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dssa",
+              "summary": "{\n  \"name\": \"dssa\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8180.650972978378,\n        \"min\": 910.0,\n        \"max\": 42350.0,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          28700.0,\n          2227.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8370.34990700453,\n        \"min\": 920.0,\n        \"max\": 44775.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          4270.0,\n          1620.0,\n          13050.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8089.417253892426,\n        \"min\": 910.0,\n        \"max\": 41550.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          3815.0,\n          2087.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 510893,\n        \"min\": 0,\n        \"max\": 12000000,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          1529000,\n          1100200,\n          1728600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa.info()"
+      ],
+      "metadata": {
+        "id": "4l_a9qfpoCar",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "21ca8c7d-3624-4790-8821-4218bf3c2392"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "qttR5RBpk9Mw",
+        "outputId": "fcc99023-754f-405b-9061-7021ad60f124"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Open          High           Low         Close     Adj Close  \\\n",
+              "count   1148.000000   1148.000000   1148.000000   1148.000000   1148.000000   \n",
+              "mean    5894.359756   5987.554443   5857.016551   5930.013066   5930.013066   \n",
+              "std     8180.650973   8370.349907   8089.417254   8228.663219   8228.663219   \n",
+              "min      910.000000    920.000000    910.000000    920.000000    920.000000   \n",
+              "25%     1750.000000   1787.500000   1750.000000   1787.500000   1787.500000   \n",
+              "50%     3675.000000   3675.000000   3675.000000   3675.000000   3675.000000   \n",
+              "75%     5000.000000   5000.000000   4995.000000   5000.000000   5000.000000   \n",
+              "max    42350.000000  44775.000000  41550.000000  41875.000000  41875.000000   \n",
+              "\n",
+              "             Volume  \n",
+              "count  1.148000e+03  \n",
+              "mean   1.141948e+05  \n",
+              "std    5.108930e+05  \n",
+              "min    0.000000e+00  \n",
+              "25%    0.000000e+00  \n",
+              "50%    1.000000e+03  \n",
+              "75%    1.000000e+04  \n",
+              "max    1.200000e+07  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d5f065cb-108b-47ae-ba5a-6d10d9800beb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1148.000000</td>\n",
+              "      <td>1.148000e+03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>5894.359756</td>\n",
+              "      <td>5987.554443</td>\n",
+              "      <td>5857.016551</td>\n",
+              "      <td>5930.013066</td>\n",
+              "      <td>5930.013066</td>\n",
+              "      <td>1.141948e+05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8180.650973</td>\n",
+              "      <td>8370.349907</td>\n",
+              "      <td>8089.417254</td>\n",
+              "      <td>8228.663219</td>\n",
+              "      <td>8228.663219</td>\n",
+              "      <td>5.108930e+05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>910.000000</td>\n",
+              "      <td>920.000000</td>\n",
+              "      <td>910.000000</td>\n",
+              "      <td>920.000000</td>\n",
+              "      <td>920.000000</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1750.000000</td>\n",
+              "      <td>1787.500000</td>\n",
+              "      <td>1750.000000</td>\n",
+              "      <td>1787.500000</td>\n",
+              "      <td>1787.500000</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3675.000000</td>\n",
+              "      <td>3675.000000</td>\n",
+              "      <td>3675.000000</td>\n",
+              "      <td>3675.000000</td>\n",
+              "      <td>3675.000000</td>\n",
+              "      <td>1.000000e+03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>4995.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>1.000000e+04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42350.000000</td>\n",
+              "      <td>44775.000000</td>\n",
+              "      <td>41550.000000</td>\n",
+              "      <td>41875.000000</td>\n",
+              "      <td>41875.000000</td>\n",
+              "      <td>1.200000e+07</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5f065cb-108b-47ae-ba5a-6d10d9800beb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d5f065cb-108b-47ae-ba5a-6d10d9800beb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d5f065cb-108b-47ae-ba5a-6d10d9800beb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-87f91b24-55f0-4d8a-9f9a-ede6ce53d3ab\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-87f91b24-55f0-4d8a-9f9a-ede6ce53d3ab')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-87f91b24-55f0-4d8a-9f9a-ede6ce53d3ab button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"dssa\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13863.435533013706,\n        \"min\": 910.0,\n        \"max\": 42350.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5894.359756097561,\n          3675.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14699.970046847362,\n        \"min\": 920.0,\n        \"max\": 44775.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5987.554442508711,\n          3675.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13586.992266774962,\n        \"min\": 910.0,\n        \"max\": 41550.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5857.016550522648,\n          3675.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13693.720562353656,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5930.01306620209,\n          3675.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13693.720562353656,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5930.01306620209,\n          3675.0,\n          1148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4214120.622032018,\n        \"min\": 0.0,\n        \"max\": 12000000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1148.0,\n          114194.77351916376,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_stock_data(dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 435
+        },
+        "id": "zRosG3uYnKdr",
+        "outputId": "a2d8b199-9cae-4d38-a80c-3789884077c7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_histogram(dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 898
+        },
+        "id": "wHoP6YKa12Ze",
+        "outputId": "6adb2de3-bf7b-4480-840e-bf31491426cd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x1200 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_correlation_heatmap(dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        },
+        "id": "eYNjr-huoPBh",
+        "outputId": "d06bc73e-d454-47af-e94f-161c9d66037e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Cleaning Dataset"
+      ],
+      "metadata": {
+        "id": "PcdKQXbg5jJW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_missing = adro.isnull().sum()\n",
+        "dssa_missing = dssa.isnull().sum()\n",
+        "\n",
+        "print(\"Missing values in ADRO:\")\n",
+        "print(adro_missing)\n",
+        "print(\"\\nMissing values in DSSA:\")\n",
+        "print(dssa_missing)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BivImi0n5eP2",
+        "outputId": "a55dac2f-1acc-450c-a0d3-10b664987355"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values in ADRO:\n",
+            "Date         0\n",
+            "Open         0\n",
+            "High         0\n",
+            "Low          0\n",
+            "Close        0\n",
+            "Adj Close    0\n",
+            "Volume       0\n",
+            "dtype: int64\n",
+            "\n",
+            "Missing values in DSSA:\n",
+            "Date         0\n",
+            "Open         0\n",
+            "High         0\n",
+            "Low          0\n",
+            "Close        0\n",
+            "Adj Close    0\n",
+            "Volume       0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Data duplicate in ADRO: \", adro.duplicated().sum())\n",
+        "print(\"Data duplicate in DSSA: \", dssa.duplicated().sum())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1BnDL-WHmsJX",
+        "outputId": "7ea35df3-a9d4-4d44-906a-9770e2264afa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data duplicate in ADRO:  0\n",
+            "Data duplicate in DSSA:  0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/GRU.ipynb b/GRU.ipynb
new file mode 100644
index 0000000..a34752b
--- /dev/null
+++ b/GRU.ipynb
@@ -0,0 +1,3866 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import Libraries"
+      ],
+      "metadata": {
+        "id": "XTBQCgfi4s_3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "13IWG7jegtHo"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import missingno as msno\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import GRU, Dense, Dropout\n",
+        "from tensorflow.keras.callbacks import EarlyStopping\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Reading Dataset"
+      ],
+      "metadata": {
+        "id": "_XF8Df9v4v3C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ADRO"
+      ],
+      "metadata": {
+        "id": "f30_aaa44yhE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_adro = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Adaro%20Energy%20Indonesia%20Tbk_2020-2024.csv\"\n",
+        "adro = pd.read_csv(url_adro)\n",
+        "adro.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "CJ69vEFiDxO3",
+        "outputId": "584cb3a9-69a0-4ae6-d4be-f1842014eac2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close   Adj Close     Volume\n",
+              "0  2020-01-02  1555.0  1555.0  1490.0  1495.0  835.632202   76612000\n",
+              "1  2020-01-03  1460.0  1470.0  1425.0  1465.0  856.372375  117795600\n",
+              "2  2020-01-06  1455.0  1515.0  1450.0  1465.0  856.372375   61423200\n",
+              "3  2020-01-07  1480.0  1540.0  1460.0  1540.0  900.213867   74336500\n",
+              "4  2020-01-08  1530.0  1535.0  1505.0  1505.0  879.754395   55121100"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d3121271-5570-49ef-8866-5f4893d046f6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1490.0</td>\n",
+              "      <td>1495.0</td>\n",
+              "      <td>835.632202</td>\n",
+              "      <td>76612000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1470.0</td>\n",
+              "      <td>1425.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>117795600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1455.0</td>\n",
+              "      <td>1515.0</td>\n",
+              "      <td>1450.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>61423200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1480.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>900.213867</td>\n",
+              "      <td>74336500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1530.0</td>\n",
+              "      <td>1535.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>879.754395</td>\n",
+              "      <td>55121100</td>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d3121271-5570-49ef-8866-5f4893d046f6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d3121271-5570-49ef-8866-5f4893d046f6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a934c254-b633-4405-a7fe-bdd49bf6aef3 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro",
+              "summary": "{\n  \"name\": \"adro\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 913.246493337149,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          3120.0,\n          1670.0,\n          3090.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 924.8420313199422,\n        \"min\": 670.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          3020.0,\n          1930.0,\n          3320.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 902.619092508634,\n        \"min\": 610.0,\n        \"max\": 4080.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          2870.0,\n          1900.0,\n          3120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 914.3952655818309,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          3290.0,\n          775.0,\n          3640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 865.5150705162999,\n        \"min\": 377.03759765625,\n        \"max\": 3910.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          2913.447509765625,\n          2624.987548828125,\n          776.3223266601562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68712203,\n        \"min\": 0,\n        \"max\": 539995200,\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          57485800,\n          121344700,\n          40850500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B7p8nU8okoXL",
+        "outputId": "0c3b527b-9aaa-4ca8-c6bb-819260a61d9c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DSSA"
+      ],
+      "metadata": {
+        "id": "PTWSX4dM5fsn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_dssa = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Dian%20Swastatika%20Sentosa%20Tbk_2020-2024.csv\"\n",
+        "dssa = pd.read_csv(url_dssa)\n",
+        "dssa.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "opJl-3DOFICJ",
+        "outputId": "d248de8f-3d68-42e2-c2c3-8b1ee6f5324c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close  Adj Close  Volume\n",
+              "0  2020-01-02  1360.0  1435.0  1360.0  1435.0     1435.0    5000\n",
+              "1  2020-01-03  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "2  2020-01-06  1437.5  1437.5  1435.0  1435.0     1435.0    3000\n",
+              "3  2020-01-07  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "4  2020-01-08  1435.0  1435.0  1435.0  1435.0     1435.0       0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-df34a22f-ffcc-4722-a9dc-7f94a00f6d3e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>3000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df34a22f-ffcc-4722-a9dc-7f94a00f6d3e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-df34a22f-ffcc-4722-a9dc-7f94a00f6d3e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-df34a22f-ffcc-4722-a9dc-7f94a00f6d3e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f7b0dcb2-aba8-4ac2-a0e3-be8644b81ed5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7b0dcb2-aba8-4ac2-a0e3-be8644b81ed5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f7b0dcb2-aba8-4ac2-a0e3-be8644b81ed5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dssa",
+              "summary": "{\n  \"name\": \"dssa\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8180.650972978378,\n        \"min\": 910.0,\n        \"max\": 42350.0,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          28700.0,\n          2227.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8370.34990700453,\n        \"min\": 920.0,\n        \"max\": 44775.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          4270.0,\n          1620.0,\n          13050.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8089.417253892426,\n        \"min\": 910.0,\n        \"max\": 41550.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          3815.0,\n          2087.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 510893,\n        \"min\": 0,\n        \"max\": 12000000,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          1529000,\n          1100200,\n          1728600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa.info()"
+      ],
+      "metadata": {
+        "id": "4l_a9qfpoCar",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f685ea9a-1831-401f-b9e7-6aee685453f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Pre-processing Data"
+      ],
+      "metadata": {
+        "id": "GiaxLakO6sVo"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Cleaning Dataset"
+      ],
+      "metadata": {
+        "id": "PcdKQXbg5jJW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_missing = adro.isnull().sum()\n",
+        "dssa_missing = dssa.isnull().sum()\n",
+        "\n",
+        "print(\"Missing values in ADRO:\")\n",
+        "print(adro_missing)\n",
+        "print(\"\\nMissing values in DSSA:\")\n",
+        "print(dssa_missing)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BivImi0n5eP2",
+        "outputId": "d12252d6-22f1-4e85-9747-3e9483c68340"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values in ADRO:\n",
+            "Date         0\n",
+            "Open         0\n",
+            "High         0\n",
+            "Low          0\n",
+            "Close        0\n",
+            "Adj Close    0\n",
+            "Volume       0\n",
+            "dtype: int64\n",
+            "\n",
+            "Missing values in DSSA:\n",
+            "Date         0\n",
+            "Open         0\n",
+            "High         0\n",
+            "Low          0\n",
+            "Close        0\n",
+            "Adj Close    0\n",
+            "Volume       0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Normalisasi Data"
+      ],
+      "metadata": {
+        "id": "scobtTMV6p5p"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*   Normalisasi data pake MinMaxScaler\n",
+        "*   Kolom Date Ga dipake"
+      ],
+      "metadata": {
+        "id": "bKVujuOSIv-e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = MinMaxScaler()"
+      ],
+      "metadata": {
+        "id": "AEAlTru7kzRs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def normalize_data(df):\n",
+        "    scaled_df = pd.DataFrame(scaler.fit_transform(df.iloc[:, 1:]), columns=df.columns[1:], index=df.index)\n",
+        "    return scaled_df"
+      ],
+      "metadata": {
+        "id": "sKSDpg6E9BC8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_norm = adro.copy()\n",
+        "adro_norm = adro_norm.drop('Volume', axis=1)\n",
+        "adro_norm = normalize_data(adro_norm)\n",
+        "adro_norm.head()"
+      ],
+      "metadata": {
+        "id": "ljGbAlvglUB5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "3f91083a-a5c6-4b6f-8c3d-1f734e6e7fc6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Open      High       Low     Close  Adj Close\n",
+              "0  0.260372  0.247207  0.253602  0.243205   0.129805\n",
+              "1  0.233190  0.223464  0.234870  0.234621   0.135675\n",
+              "2  0.231760  0.236034  0.242075  0.234621   0.135675\n",
+              "3  0.238913  0.243017  0.244957  0.256080   0.148084\n",
+              "4  0.253219  0.241620  0.257925  0.246066   0.142293"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-599b5006-3dbf-438a-b3d8-d1484371029f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.260372</td>\n",
+              "      <td>0.247207</td>\n",
+              "      <td>0.253602</td>\n",
+              "      <td>0.243205</td>\n",
+              "      <td>0.129805</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.233190</td>\n",
+              "      <td>0.223464</td>\n",
+              "      <td>0.234870</td>\n",
+              "      <td>0.234621</td>\n",
+              "      <td>0.135675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.231760</td>\n",
+              "      <td>0.236034</td>\n",
+              "      <td>0.242075</td>\n",
+              "      <td>0.234621</td>\n",
+              "      <td>0.135675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.238913</td>\n",
+              "      <td>0.243017</td>\n",
+              "      <td>0.244957</td>\n",
+              "      <td>0.256080</td>\n",
+              "      <td>0.148084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.253219</td>\n",
+              "      <td>0.241620</td>\n",
+              "      <td>0.257925</td>\n",
+              "      <td>0.246066</td>\n",
+              "      <td>0.142293</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-599b5006-3dbf-438a-b3d8-d1484371029f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-599b5006-3dbf-438a-b3d8-d1484371029f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-599b5006-3dbf-438a-b3d8-d1484371029f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-65ab4087-7a41-4ebb-9303-5354e167d7a5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65ab4087-7a41-4ebb-9303-5354e167d7a5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-65ab4087-7a41-4ebb-9303-5354e167d7a5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro_norm",
+              "summary": "{\n  \"name\": \"adro_norm\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2613008564627031,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          0.7081545064377683,\n          0.2932761087267525,\n          0.6995708154506439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2583357629385311,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          0.6564245810055865,\n          0.35195530726256974,\n          0.7402234636871508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2601207759390876,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          0.6512968299711817,\n          0.3717579250720462,\n          0.723342939481268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26162954666146787,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          0.7567954220314737,\n          0.037195994277539335,\n          0.8569384835479257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24498281383977363,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          0.7179272302549082,\n          0.6362790472042941,\n          0.11301697655741333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa_norm = dssa.copy()\n",
+        "dssa_norm = dssa_norm.drop('Volume', axis=1)\n",
+        "dssa_norm = normalize_data(dssa_norm)\n",
+        "dssa_norm.head()"
+      ],
+      "metadata": {
+        "id": "GFoIHb2x-ewb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "87e6cf24-003e-4213-85c7-1ce1120ffc6d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Open      High       Low     Close  Adj Close\n",
+              "0  0.010859  0.011743  0.011073  0.012575   0.012575\n",
+              "1  0.012669  0.011743  0.012918  0.012575   0.012575\n",
+              "2  0.012729  0.011800  0.012918  0.012575   0.012575\n",
+              "3  0.012669  0.011743  0.012918  0.012575   0.012575\n",
+              "4  0.012669  0.011743  0.012918  0.012575   0.012575"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3655b34f-8b01-43f3-9adc-62749de4a1a5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.010859</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.011073</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.012729</td>\n",
+              "      <td>0.011800</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3655b34f-8b01-43f3-9adc-62749de4a1a5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3655b34f-8b01-43f3-9adc-62749de4a1a5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3655b34f-8b01-43f3-9adc-62749de4a1a5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3b3dee2e-c04a-42a9-a43d-d3d6ed63ab6f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b3dee2e-c04a-42a9-a43d-d3d6ed63ab6f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3b3dee2e-c04a-42a9-a43d-d3d6ed63ab6f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dssa_norm",
+              "summary": "{\n  \"name\": \"dssa_norm\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19740953120121557,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          0.6706081081081081,\n          0.03179295366795366,\n          0.02931949806949807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1908642094859088,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          0.07638809713829667,\n          0.01596169193934557,\n          0.27659331889180255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19905062140483315,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          0.07148129921259842,\n          0.028973917322834646,\n          0.029896653543307086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20091962443167757,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          0.015382737150531074,\n          0.06037113905506044,\n          0.08985471859357833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20091962443167757,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          0.015382737150531074,\n          0.06037113905506044,\n          0.08985471859357833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Windowing (Time Series Data)"
+      ],
+      "metadata": {
+        "id": "4Tzoy_Q9IXlN"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Model GRU memerlukan data time series, jadi kita perlu membuat window (misalnya 30 hari ke belakang untuk memprediksi harga di hari ke-31).\n"
+      ],
+      "metadata": {
+        "id": "ADXEkQcSUiyj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def prepare_data(df, time_step):\n",
+        "    X, y = [], []\n",
+        "    for i in range(len(df)-time_step):\n",
+        "        t = []\n",
+        "        for j in range(time_step):\n",
+        "            t.append(df.iloc[i + j].values)  # Use all columns for features\n",
+        "        X.append(t)\n",
+        "        y.append(df['High'][i + time_step]) # Predict High price\n",
+        "    return np.array(X), np.array(y)"
+      ],
+      "metadata": {
+        "id": "PEwwCBEHs9K8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Mining"
+      ],
+      "metadata": {
+        "id": "_vI3046ViEpy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ADRO\n"
+      ],
+      "metadata": {
+        "id": "lGHmwVRWVPSq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "time_step = 7\n",
+        "X_adro, y_adro = prepare_data(adro_norm, time_step)"
+      ],
+      "metadata": {
+        "id": "DDjozXGEtAol"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X_adro[0])\n",
+        "print(y_adro[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BAKLtf5wdL36",
+        "outputId": "05ba754b-d5d4-48d6-ea43-e388eccdd1c0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0.26037196 0.2472067  0.25360231 0.24320458 0.12980455]\n",
+            " [0.23319027 0.22346369 0.23487032 0.23462089 0.13567503]\n",
+            " [0.23175966 0.23603352 0.24207493 0.23462089 0.13567503]\n",
+            " [0.23891273 0.24301676 0.24495677 0.25608011 0.1480843 ]\n",
+            " [0.25321888 0.24162011 0.25792507 0.24606581 0.14229328]\n",
+            " [0.25035765 0.23882682 0.24927954 0.24320458 0.14063873]\n",
+            " [0.25035765 0.25       0.26080692 0.25894134 0.14973887]]\n",
+            "0.2583798882681564\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Target Attribute\n",
+        "\n",
+        "Dipilih atribut \"high\" sebagai target prediksi"
+      ],
+      "metadata": {
+        "id": "3KPMX8ptLK6F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro[\"High\"][:918].plot(figsize=(16,4),legend=True)\n",
+        "adro[\"High\"][918:].plot(figsize=(16,4),legend=True)\n",
+        "plt.legend(['Training set','Test set'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "d6dVRhpZLHJp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "outputId": "a54fb6d5-67c8-4734-e2b4-2feb8088035f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_adro, X_test_adro, y_train_adro, y_test_adro = train_test_split(X_adro, y_adro, test_size=0.2, random_state=42, shuffle=False)"
+      ],
+      "metadata": {
+        "id": "PgvMNs99VlKi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training Model"
+      ],
+      "metadata": {
+        "id": "aKOSaBuNWlqz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_gru_model(units):\n",
+        "    model = Sequential()\n",
+        "    model.add(GRU(units=units, return_sequences=True, input_shape=(X_train_adro.shape[1], X_train_adro.shape[2])))\n",
+        "    model.add(Dropout(0.2))\n",
+        "    model.add(GRU(units=units))\n",
+        "    model.add(Dropout(0.2))\n",
+        "    model.add(Dense(units=1)) # Output layer with 1 neuron for regression\n",
+        "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
+        "    return model"
+      ],
+      "metadata": {
+        "id": "jRzT17pX6qGg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create model\n",
+        "gru_model = create_gru_model(50)  # Example: 50 GRU units"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hY9sMu-KXkH2",
+        "outputId": "21ee804c-feab-4b0c-c55f-c6ade380f86c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_gru_adro = create_gru_model(100)\n",
+        "\n",
+        "start_time = time.time()\n",
+        "history = model_gru_adro.fit(X_train_adro, y_train_adro, epochs=100, batch_size=32, validation_split=0.1)\n",
+        "\n",
+        "end_time = time.time()\n",
+        "elapsed_time = end_time - start_time\n",
+        "\n",
+        "print(f\"Waktu training: {elapsed_time:.2f} detik\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vkVXtzaM63Yq",
+        "outputId": "c0444510-62ef-45b0-f18a-2d2d36877488"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 44ms/step - loss: 0.0577 - val_loss: 0.0021\n",
+            "Epoch 2/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0045 - val_loss: 4.9115e-04\n",
+            "Epoch 3/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0035 - val_loss: 9.7674e-04\n",
+            "Epoch 4/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0028 - val_loss: 9.5480e-04\n",
+            "Epoch 5/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0026 - val_loss: 9.4335e-04\n",
+            "Epoch 6/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0028 - val_loss: 9.0496e-04\n",
+            "Epoch 7/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0020 - val_loss: 4.3726e-04\n",
+            "Epoch 8/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0019 - val_loss: 7.1848e-04\n",
+            "Epoch 9/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0019 - val_loss: 0.0010\n",
+            "Epoch 10/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0021 - val_loss: 6.9643e-04\n",
+            "Epoch 11/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0020 - val_loss: 4.1383e-04\n",
+            "Epoch 12/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0018 - val_loss: 7.0771e-04\n",
+            "Epoch 13/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0021 - val_loss: 0.0014\n",
+            "Epoch 14/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0018 - val_loss: 0.0016\n",
+            "Epoch 15/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0018 - val_loss: 4.9007e-04\n",
+            "Epoch 16/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0016 - val_loss: 3.9633e-04\n",
+            "Epoch 17/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0016 - val_loss: 3.9178e-04\n",
+            "Epoch 18/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0013 - val_loss: 4.1424e-04\n",
+            "Epoch 19/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0017 - val_loss: 3.9486e-04\n",
+            "Epoch 20/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0015 - val_loss: 4.3281e-04\n",
+            "Epoch 21/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0017 - val_loss: 4.1300e-04\n",
+            "Epoch 22/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0015 - val_loss: 3.3882e-04\n",
+            "Epoch 23/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0015 - val_loss: 4.2586e-04\n",
+            "Epoch 24/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0016 - val_loss: 3.5654e-04\n",
+            "Epoch 25/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.0016 - val_loss: 3.6712e-04\n",
+            "Epoch 26/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0014 - val_loss: 7.0714e-04\n",
+            "Epoch 27/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0016 - val_loss: 0.0021\n",
+            "Epoch 28/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0016 - val_loss: 3.8275e-04\n",
+            "Epoch 29/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0017 - val_loss: 3.5382e-04\n",
+            "Epoch 30/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0016 - val_loss: 0.0011\n",
+            "Epoch 31/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0020 - val_loss: 0.0018\n",
+            "Epoch 32/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0020 - val_loss: 5.0929e-04\n",
+            "Epoch 33/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0013 - val_loss: 5.0779e-04\n",
+            "Epoch 34/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0013 - val_loss: 2.8508e-04\n",
+            "Epoch 35/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0012 - val_loss: 5.2215e-04\n",
+            "Epoch 36/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0013 - val_loss: 3.1494e-04\n",
+            "Epoch 37/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0015 - val_loss: 4.8841e-04\n",
+            "Epoch 38/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0014 - val_loss: 3.9487e-04\n",
+            "Epoch 39/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0011 - val_loss: 2.8669e-04\n",
+            "Epoch 40/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0014 - val_loss: 7.1787e-04\n",
+            "Epoch 41/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0013 - val_loss: 2.8234e-04\n",
+            "Epoch 42/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0012 - val_loss: 4.7626e-04\n",
+            "Epoch 43/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0013 - val_loss: 6.2097e-04\n",
+            "Epoch 44/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0016 - val_loss: 0.0019\n",
+            "Epoch 45/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0016 - val_loss: 3.4984e-04\n",
+            "Epoch 46/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0014 - val_loss: 4.4263e-04\n",
+            "Epoch 47/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 0.0014 - val_loss: 6.1408e-04\n",
+            "Epoch 48/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0014 - val_loss: 2.2885e-04\n",
+            "Epoch 49/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0012 - val_loss: 2.2621e-04\n",
+            "Epoch 50/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0011 - val_loss: 3.2538e-04\n",
+            "Epoch 51/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0011 - val_loss: 2.5797e-04\n",
+            "Epoch 52/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 0.0011 - val_loss: 4.8794e-04\n",
+            "Epoch 53/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0012 - val_loss: 4.5312e-04\n",
+            "Epoch 54/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0011 - val_loss: 4.5782e-04\n",
+            "Epoch 55/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0010 - val_loss: 2.2918e-04\n",
+            "Epoch 56/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0011 - val_loss: 2.1239e-04\n",
+            "Epoch 57/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0011 - val_loss: 4.0432e-04\n",
+            "Epoch 58/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0012 - val_loss: 5.5354e-04\n",
+            "Epoch 59/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0011 - val_loss: 2.3901e-04\n",
+            "Epoch 60/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0012 - val_loss: 5.7147e-04\n",
+            "Epoch 61/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0013 - val_loss: 5.6428e-04\n",
+            "Epoch 62/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0011 - val_loss: 8.3079e-04\n",
+            "Epoch 63/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0012 - val_loss: 2.2646e-04\n",
+            "Epoch 64/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0012 - val_loss: 2.1170e-04\n",
+            "Epoch 65/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0011 - val_loss: 2.6009e-04\n",
+            "Epoch 66/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0010 - val_loss: 3.2673e-04\n",
+            "Epoch 67/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0013 - val_loss: 2.1459e-04\n",
+            "Epoch 68/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0012 - val_loss: 4.3743e-04\n",
+            "Epoch 69/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0011 - val_loss: 3.2609e-04\n",
+            "Epoch 70/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0012 - val_loss: 1.8397e-04\n",
+            "Epoch 71/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 9.3354e-04 - val_loss: 3.2544e-04\n",
+            "Epoch 72/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0012 - val_loss: 3.2258e-04\n",
+            "Epoch 73/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 9.3039e-04 - val_loss: 2.1268e-04\n",
+            "Epoch 74/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 7.9803e-04 - val_loss: 4.3960e-04\n",
+            "Epoch 75/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 9.3897e-04 - val_loss: 1.9310e-04\n",
+            "Epoch 76/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0010 - val_loss: 2.1357e-04\n",
+            "Epoch 77/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 9.9517e-04 - val_loss: 2.0085e-04\n",
+            "Epoch 78/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 9.9405e-04 - val_loss: 3.0909e-04\n",
+            "Epoch 79/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0010 - val_loss: 3.3926e-04\n",
+            "Epoch 80/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 9.7004e-04 - val_loss: 1.8530e-04\n",
+            "Epoch 81/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 8.9757e-04 - val_loss: 1.9034e-04\n",
+            "Epoch 82/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 8.7562e-04 - val_loss: 0.0011\n",
+            "Epoch 83/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0015 - val_loss: 1.7253e-04\n",
+            "Epoch 84/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 8.6734e-04 - val_loss: 1.8017e-04\n",
+            "Epoch 85/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 8.6932e-04 - val_loss: 1.7235e-04\n",
+            "Epoch 86/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0011 - val_loss: 4.5892e-04\n",
+            "Epoch 87/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0013 - val_loss: 4.9913e-04\n",
+            "Epoch 88/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 8.9259e-04 - val_loss: 1.7769e-04\n",
+            "Epoch 89/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 8.4344e-04 - val_loss: 3.6119e-04\n",
+            "Epoch 90/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 7.8751e-04 - val_loss: 4.7063e-04\n",
+            "Epoch 91/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0011 - val_loss: 8.1291e-04\n",
+            "Epoch 92/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - loss: 9.6640e-04 - val_loss: 3.6469e-04\n",
+            "Epoch 93/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0010 - val_loss: 2.0196e-04\n",
+            "Epoch 94/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0011 - val_loss: 2.2020e-04\n",
+            "Epoch 95/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 8.2097e-04 - val_loss: 3.7359e-04\n",
+            "Epoch 96/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - loss: 0.0011 - val_loss: 3.6962e-04\n",
+            "Epoch 97/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 66ms/step - loss: 0.0010 - val_loss: 6.0087e-04\n",
+            "Epoch 98/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 9.1164e-04 - val_loss: 4.5958e-04\n",
+            "Epoch 99/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0014 - val_loss: 2.4420e-04\n",
+            "Epoch 100/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 9.4386e-04 - val_loss: 1.5957e-04\n",
+            "Waktu training: 77.03 detik\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_gru_adro.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 302
+        },
+        "id": "_GjbaRRBZvJd",
+        "outputId": "22dcfa5a-2751-481d-dd3c-3843981e4bf0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gru_2 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │        \u001b[38;5;34m32,100\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ gru_3 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gru_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,100</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ gru_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">60,600</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m278,405\u001b[0m (1.06 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">278,405</span> (1.06 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m92,801\u001b[0m (362.50 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">92,801</span> (362.50 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m185,604\u001b[0m (725.02 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">185,604</span> (725.02 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Plot training history"
+      ],
+      "metadata": {
+        "id": "nSFbegfgWrZq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend(loc='upper right')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "ZkGX88gnWRaR",
+        "outputId": "3865635c-29ea-49a2-e968-62be83c22df3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prediction"
+      ],
+      "metadata": {
+        "id": "8b-qUyvwXHcl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_adro = model_gru_adro.predict(X_test_adro)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mepO7OcrWRce",
+        "outputId": "5d76f010-7b7b-4101-8557-def9e969a49e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 68ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# DSSA"
+      ],
+      "metadata": {
+        "id": "YQN0JJHuYNt2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "time_step = 7\n",
+        "X_dssa, y_dssa = prepare_data(dssa_norm, time_step)"
+      ],
+      "metadata": {
+        "id": "o7LuYgWatEoi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Target Attribute"
+      ],
+      "metadata": {
+        "id": "r9IZr0-SYNv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa[\"High\"][:918].plot(figsize=(16,4),legend=True)\n",
+        "dssa[\"High\"][918:].plot(figsize=(16,4),legend=True)\n",
+        "plt.legend(['Training set','Test set'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 328
+        },
+        "id": "uneVAuClXJtX",
+        "outputId": "0f474c09-f169-4ee6-c523-ec62640d99ca"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_dssa, X_test_dssa, y_train_dssa, y_test_dssa = train_test_split(X_dssa, y_dssa, test_size=0.2, random_state=42, shuffle=False)"
+      ],
+      "metadata": {
+        "id": "tmBpUdQwXJvP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training Model"
+      ],
+      "metadata": {
+        "id": "sX4EhWfQZAeF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_gru_dssa = create_gru_model(100)\n",
+        "\n",
+        "start_time = time.time()\n",
+        "history = model_gru_dssa.fit(X_train_dssa, y_train_dssa, epochs=100, batch_size=32, validation_split=0.1)\n",
+        "\n",
+        "end_time = time.time()\n",
+        "elapsed_time = end_time - start_time\n",
+        "\n",
+        "print(f\"Waktu training: {elapsed_time:.2f} detik\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "54SsB3jhB6XX",
+        "outputId": "3cefa1a4-cb3d-4903-a6b3-538f97752edd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - loss: 0.0010 - val_loss: 1.3904e-05\n",
+            "Epoch 2/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 7.0119e-05 - val_loss: 5.8614e-05\n",
+            "Epoch 3/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 3.6273e-05 - val_loss: 1.6406e-05\n",
+            "Epoch 4/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 3.0979e-05 - val_loss: 1.1849e-05\n",
+            "Epoch 5/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.4063e-05 - val_loss: 1.4732e-05\n",
+            "Epoch 6/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 2.5219e-05 - val_loss: 1.4240e-05\n",
+            "Epoch 7/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.9691e-05 - val_loss: 1.1127e-05\n",
+            "Epoch 8/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.6204e-05 - val_loss: 1.0614e-05\n",
+            "Epoch 9/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 2.8645e-05 - val_loss: 1.0287e-05\n",
+            "Epoch 10/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 2.2182e-05 - val_loss: 1.0952e-05\n",
+            "Epoch 11/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 2.4807e-05 - val_loss: 1.3093e-05\n",
+            "Epoch 12/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 3.0003e-05 - val_loss: 1.2617e-05\n",
+            "Epoch 13/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.1352e-05 - val_loss: 9.9505e-06\n",
+            "Epoch 14/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.9796e-05 - val_loss: 8.8470e-06\n",
+            "Epoch 15/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 2.1070e-05 - val_loss: 9.2233e-06\n",
+            "Epoch 16/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.5897e-05 - val_loss: 1.1264e-05\n",
+            "Epoch 17/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 2.1497e-05 - val_loss: 8.2220e-06\n",
+            "Epoch 18/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 1.9787e-05 - val_loss: 2.3476e-05\n",
+            "Epoch 19/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 2.1493e-05 - val_loss: 3.0296e-05\n",
+            "Epoch 20/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 2.3328e-05 - val_loss: 1.3040e-05\n",
+            "Epoch 21/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 2.0738e-05 - val_loss: 7.6789e-06\n",
+            "Epoch 22/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.7374e-05 - val_loss: 4.2427e-05\n",
+            "Epoch 23/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 2.2584e-05 - val_loss: 8.9704e-06\n",
+            "Epoch 24/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 2.2772e-05 - val_loss: 1.5896e-05\n",
+            "Epoch 25/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 2.1244e-05 - val_loss: 1.3325e-05\n",
+            "Epoch 26/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.8138e-05 - val_loss: 6.6058e-06\n",
+            "Epoch 27/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 2.1266e-05 - val_loss: 1.3053e-05\n",
+            "Epoch 28/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 2.7409e-05 - val_loss: 7.5037e-06\n",
+            "Epoch 29/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 2.1759e-05 - val_loss: 1.4107e-05\n",
+            "Epoch 30/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 2.1703e-05 - val_loss: 7.2739e-06\n",
+            "Epoch 31/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.9047e-05 - val_loss: 8.9616e-06\n",
+            "Epoch 32/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.8571e-05 - val_loss: 6.1350e-06\n",
+            "Epoch 33/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 2.1641e-05 - val_loss: 9.2258e-06\n",
+            "Epoch 34/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.4440e-05 - val_loss: 7.2591e-06\n",
+            "Epoch 35/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.1740e-05 - val_loss: 8.2017e-06\n",
+            "Epoch 36/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.5298e-05 - val_loss: 5.9470e-06\n",
+            "Epoch 37/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.3562e-05 - val_loss: 8.1021e-06\n",
+            "Epoch 38/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 1.6456e-05 - val_loss: 5.9867e-06\n",
+            "Epoch 39/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.5795e-05 - val_loss: 5.7918e-06\n",
+            "Epoch 40/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.6047e-05 - val_loss: 8.4087e-06\n",
+            "Epoch 41/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.8081e-05 - val_loss: 1.1000e-05\n",
+            "Epoch 42/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.2178e-05 - val_loss: 5.0737e-06\n",
+            "Epoch 43/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.0312e-05 - val_loss: 5.1361e-06\n",
+            "Epoch 44/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.3883e-05 - val_loss: 7.9759e-06\n",
+            "Epoch 45/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.3585e-05 - val_loss: 5.2223e-06\n",
+            "Epoch 46/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.4344e-05 - val_loss: 6.6634e-06\n",
+            "Epoch 47/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.6109e-05 - val_loss: 6.4313e-06\n",
+            "Epoch 48/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.4422e-05 - val_loss: 1.0266e-05\n",
+            "Epoch 49/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.3127e-05 - val_loss: 7.4248e-06\n",
+            "Epoch 50/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.4098e-05 - val_loss: 1.7432e-05\n",
+            "Epoch 51/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.5665e-05 - val_loss: 5.0212e-06\n",
+            "Epoch 52/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.3220e-05 - val_loss: 1.8317e-05\n",
+            "Epoch 53/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.4225e-05 - val_loss: 1.0309e-05\n",
+            "Epoch 54/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 2.0958e-05 - val_loss: 8.0765e-06\n",
+            "Epoch 55/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.7236e-05 - val_loss: 5.0938e-06\n",
+            "Epoch 56/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 1.7097e-05 - val_loss: 9.8849e-06\n",
+            "Epoch 57/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 1.5159e-05 - val_loss: 4.8778e-06\n",
+            "Epoch 58/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.0558e-05 - val_loss: 9.1439e-06\n",
+            "Epoch 59/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.8162e-05 - val_loss: 4.7532e-06\n",
+            "Epoch 60/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.4689e-05 - val_loss: 5.4592e-06\n",
+            "Epoch 61/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.3971e-05 - val_loss: 7.9401e-06\n",
+            "Epoch 62/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.8781e-05 - val_loss: 4.5423e-06\n",
+            "Epoch 63/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.5364e-05 - val_loss: 4.2929e-06\n",
+            "Epoch 64/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.0846e-05 - val_loss: 4.1504e-06\n",
+            "Epoch 65/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.5690e-05 - val_loss: 4.9555e-06\n",
+            "Epoch 66/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.1853e-05 - val_loss: 8.0127e-06\n",
+            "Epoch 67/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.2843e-05 - val_loss: 5.5549e-06\n",
+            "Epoch 68/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.2391e-05 - val_loss: 4.5328e-06\n",
+            "Epoch 69/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.0328e-05 - val_loss: 5.0042e-06\n",
+            "Epoch 70/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.3562e-05 - val_loss: 1.5186e-05\n",
+            "Epoch 71/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.4885e-05 - val_loss: 3.9889e-06\n",
+            "Epoch 72/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.3049e-05 - val_loss: 1.3736e-05\n",
+            "Epoch 73/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.6799e-05 - val_loss: 5.5536e-06\n",
+            "Epoch 74/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.2427e-05 - val_loss: 4.5323e-06\n",
+            "Epoch 75/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 1.3558e-05 - val_loss: 1.0570e-05\n",
+            "Epoch 76/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 1.2270e-05 - val_loss: 1.2344e-05\n",
+            "Epoch 77/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.1605e-05 - val_loss: 4.0330e-06\n",
+            "Epoch 78/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.2763e-05 - val_loss: 5.2620e-06\n",
+            "Epoch 79/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.1389e-05 - val_loss: 4.9307e-06\n",
+            "Epoch 80/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.0679e-05 - val_loss: 4.5947e-06\n",
+            "Epoch 81/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.2539e-05 - val_loss: 4.1249e-06\n",
+            "Epoch 82/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.2854e-05 - val_loss: 3.9017e-06\n",
+            "Epoch 83/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.1152e-05 - val_loss: 4.0156e-06\n",
+            "Epoch 84/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.3847e-05 - val_loss: 6.0185e-06\n",
+            "Epoch 85/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.1585e-05 - val_loss: 5.1645e-06\n",
+            "Epoch 86/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.1454e-05 - val_loss: 4.0461e-06\n",
+            "Epoch 87/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.0926e-05 - val_loss: 4.2207e-06\n",
+            "Epoch 88/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.3190e-05 - val_loss: 4.0996e-06\n",
+            "Epoch 89/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 9.4316e-06 - val_loss: 4.9485e-06\n",
+            "Epoch 90/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.0185e-05 - val_loss: 4.6156e-06\n",
+            "Epoch 91/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.1784e-05 - val_loss: 3.9709e-06\n",
+            "Epoch 92/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.1216e-05 - val_loss: 4.0491e-06\n",
+            "Epoch 93/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 1.1631e-05 - val_loss: 1.5254e-05\n",
+            "Epoch 94/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 1.4119e-05 - val_loss: 3.9365e-06\n",
+            "Epoch 95/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 1.1023e-05 - val_loss: 6.1368e-06\n",
+            "Epoch 96/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 9.7256e-06 - val_loss: 1.4386e-05\n",
+            "Epoch 97/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.2083e-05 - val_loss: 5.9163e-06\n",
+            "Epoch 98/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.0165e-05 - val_loss: 4.8853e-06\n",
+            "Epoch 99/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 1.0427e-05 - val_loss: 8.8672e-06\n",
+            "Epoch 100/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.6594e-05 - val_loss: 1.0703e-05\n",
+            "Waktu training: 74.40 detik\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_gru_dssa.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 302
+        },
+        "id": "1Lnb7HP6ZsoZ",
+        "outputId": "da116e1f-749f-4036-c9b0-988d6e4aa192"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gru_4 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │        \u001b[38;5;34m32,100\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ gru_5 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ gru_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,100</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ gru_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">60,600</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m278,405\u001b[0m (1.06 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">278,405</span> (1.06 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m92,801\u001b[0m (362.50 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">92,801</span> (362.50 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m185,604\u001b[0m (725.02 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">185,604</span> (725.02 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Plot training history"
+      ],
+      "metadata": {
+        "id": "TLne89uzZFpo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend(loc='upper right')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "CMfKu88fY9bN",
+        "outputId": "450c500b-4201-4547-ebac-1c33846b66a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Prediction"
+      ],
+      "metadata": {
+        "id": "9HAzJVX2ZQh_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_dssa = model_gru_dssa.predict(X_test_dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GD3tLrevZIfp",
+        "outputId": "9c8475ff-71fe-4935-d3df-f8814d300e04"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 75ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Evaluasi Model"
+      ],
+      "metadata": {
+        "id": "tvhL7dyI8D6o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def evaluate_model(y_true, y_pred):\n",
+        "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
+        "    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
+        "    r2 = r2_score(y_true, y_pred)\n",
+        "\n",
+        "    print(f'RMSE: {rmse:.3f}')\n",
+        "    print(f'MAPE: {mape:.3f}%')\n",
+        "    print(f'R-squared: {r2:.5f}')\n",
+        "\n",
+        "    return rmse, mape, r2"
+      ],
+      "metadata": {
+        "id": "pFkrWMJ37626"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate ADRO model\n",
+        "print(\"Evaluation for ADRO:\")\n",
+        "rmse_adro, mape_adro, r2_adro = evaluate_model(y_test_adro, y_pred_adro)\n",
+        "\n",
+        "# Evaluate DSSA model\n",
+        "print(\"\\nEvaluation for DSSA:\")\n",
+        "rmse_dssa, mape_dssa, r2_dssa = evaluate_model(y_test_dssa, y_pred_dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DIP9i7U179Ge",
+        "outputId": "fdbb8e89-4d2b-4cb1-c0ce-5d18bea6fc39"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluation for ADRO:\n",
+            "RMSE: 0.015\n",
+            "MAPE: 17.732%\n",
+            "R-squared: 0.97978\n",
+            "\n",
+            "Evaluation for DSSA:\n",
+            "RMSE: 0.024\n",
+            "MAPE: 119.468%\n",
+            "R-squared: 0.99261\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Invert"
+      ],
+      "metadata": {
+        "id": "hzSUZCSbCL9Z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##ADRO"
+      ],
+      "metadata": {
+        "id": "pJa4isLfCNsC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dummy_input_adro = np.zeros((len(y_pred_adro), 5))\n",
+        "dummy_input_adro[:, 1] = y_pred_adro.reshape(-1)\n",
+        "dummy_input_adro"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWQOkYIgCHyh",
+        "outputId": "cf119df7-127e-42c6-aa6c-c3b293c513f1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.        , 0.59867561, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.58274603, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.58338583, 0.        , 0.        , 0.        ],\n",
+              "       ...,\n",
+              "       [0.        , 0.86513734, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.85737336, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.87373042, 0.        , 0.        , 0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inverted_adro = scaler.inverse_transform(dummy_input_adro)\n",
+        "pred_lstm_adro_real = inverted_adro[:, 1]\n",
+        "pred_lstm_adro_real = pred_lstm_adro_real / 10\n",
+        "pred_lstm_adro_real"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aQjUEPQSCJwf",
+        "outputId": "a60961ec-8825-45f2-ec30-3d36237e9a19"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2717.49188167, 2647.63270974, 2650.43853623, 2710.21827185,\n",
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+              "       2564.34927613, 2476.62628996, 2455.55566752, 2320.5304305 ,\n",
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+              "       2574.30167389, 2572.95234686, 2583.77101153, 2658.1246292 ,\n",
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+              "       2771.33008236, 2764.32048267, 2813.36206365, 2901.83943915,\n",
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+              "       2690.32131821, 2727.75638646, 2750.70540178, 2791.44608599,\n",
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+              "       2874.66992128, 2878.25366277, 2842.80938774, 2802.38054916,\n",
+              "       2830.31935596, 2842.62170529, 2869.2161516 , 2991.21758586,\n",
+              "       3099.89827132, 3200.2791056 , 3179.94771153, 3198.4775632 ,\n",
+              "       3174.36428934, 3208.74520475, 3222.26252347, 3268.81874967,\n",
+              "       3274.0231474 , 3285.33585083, 3373.99620366, 3179.62096632,\n",
+              "       3228.07545149, 3280.61242205, 3237.68803424, 3230.90323526,\n",
+              "       3275.92140639, 3279.64943856, 3267.57241273, 3250.79025584,\n",
+              "       3327.58531374, 3328.2910834 , 3324.26924193, 3322.47815537,\n",
+              "       3407.6990574 , 3530.30745357, 3562.68921107, 3616.17923206,\n",
+              "       3631.06626672, 3639.20457506, 3671.30193353, 3732.37244344,\n",
+              "       3720.69012582, 3688.14159757, 3663.70732921, 3598.97099942,\n",
+              "       3588.61709714, 3607.70215428, 3620.7693488 , 4050.10471559,\n",
+              "       3838.81400931, 3906.01896453, 3831.63973004, 3813.1961391 ,\n",
+              "       3844.40631896, 3855.13924569, 3886.0597986 , 3852.01085633,\n",
+              "       3923.74476159])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DSSA"
+      ],
+      "metadata": {
+        "id": "CKuQRfZNCPnM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dummy_input_dssa = np.zeros((len(y_pred_dssa), 5))\n",
+        "dummy_input_dssa[:, 1] = y_pred_dssa.reshape(-1)\n",
+        "dummy_input_dssa"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JlOqbpahCQqW",
+        "outputId": "ba01b489-9be6-4d9b-a0f7-cccdc2ead39a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.        , 0.09904999, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.09905533, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.09918468, 0.        , 0.        , 0.        ],\n",
+              "       ...,\n",
+              "       [0.        , 0.9006694 , 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.89220369, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.91206467, 0.        , 0.        , 0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inverted_dssa = scaler.inverse_transform(dummy_input_dssa)\n",
+        "pred_lstm_dssa_real = inverted_dssa[:, 1]\n",
+        "pred_lstm_dssa_real"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rk8ks9rxCQsO",
+        "outputId": "569ff6f7-064a-4549-bee3-9ac0923de227"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 5263.8371101 ,  5264.07171316,  5269.74433679,  5268.91211674,\n",
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+              "       29323.20323706, 29451.18541569, 31853.28290284, 31005.13597906,\n",
+              "       31647.26089776, 31539.04288352, 31229.41912055, 31299.45761025,\n",
+              "       38365.12938827, 42503.43069017, 40207.69269049, 39307.46519387,\n",
+              "       39300.09382188, 39903.73338252, 39774.57492113, 40447.23875433,\n",
+              "       40348.98777604, 40209.6165663 , 40753.08272868, 41016.88374311,\n",
+              "       40510.27966857, 40984.49152976, 41779.71618503, 40270.07750034,\n",
+              "       40476.8314147 , 40214.65628445, 40441.86183512, 40444.41306174,\n",
+              "       40447.32240111, 40910.56346506, 40461.58156216, 40685.14063627,\n",
+              "       40581.24088675, 40204.85131413, 39634.15551275, 39252.49096543,\n",
+              "       40659.79304969, 40667.24284053, 40418.85635823, 40047.5927645 ,\n",
+              "       40918.59616935])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Simpan Prediksi"
+      ],
+      "metadata": {
+        "id": "kKqK1aekCE7v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_actual = adro['High'].iloc[919:]\n",
+        "dssa_actual = dssa['High'].iloc[919:]"
+      ],
+      "metadata": {
+        "id": "j5L61dhpCVM8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create dataframes for predictions\n",
+        "df_pred_adro = pd.DataFrame({'Actual ADRO': adro_actual.values, 'Predicted ADRO': pred_lstm_adro_real.flatten()})\n",
+        "df_pred_dssa = pd.DataFrame({'Actual DSSA': dssa_actual.values, 'Predicted DSSA': pred_lstm_dssa_real.flatten()})"
+      ],
+      "metadata": {
+        "id": "l3TYuAzlojtM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_pred_adro.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "O_3GsadQF9g9",
+        "outputId": "dc4f4031-9a82-44a2-f4f5-145cba0b992f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual ADRO  Predicted ADRO\n",
+              "224       3740.0     3844.406319\n",
+              "225       3760.0     3855.139246\n",
+              "226       3760.0     3886.059799\n",
+              "227       3780.0     3852.010856\n",
+              "228       3940.0     3923.744762"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-238f263e-2e2c-479b-98b4-922658956478\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual ADRO</th>\n",
+              "      <th>Predicted ADRO</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>3740.0</td>\n",
+              "      <td>3844.406319</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3855.139246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3886.059799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>3780.0</td>\n",
+              "      <td>3852.010856</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>3940.0</td>\n",
+              "      <td>3923.744762</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-238f263e-2e2c-479b-98b4-922658956478')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-238f263e-2e2c-479b-98b4-922658956478 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-238f263e-2e2c-479b-98b4-922658956478');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4ffc460c-9efb-413d-b8e2-ea04e28b8cc4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ffc460c-9efb-413d-b8e2-ea04e28b8cc4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4ffc460c-9efb-413d-b8e2-ea04e28b8cc4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df_pred_adro\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.7312669668102,\n        \"min\": 3740.0,\n        \"max\": 3940.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3760.0,\n          3940.0,\n          3740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.86369565366576,\n        \"min\": 3844.406318962574,\n        \"max\": 3923.7447615861893,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3855.1392456889153,\n          3923.7447615861893,\n          3886.0597985982895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_pred_dssa.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pDpLLa7qF_bx",
+        "outputId": "363a6685-0e9d-45ee-c801-c5304b56cf75"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual DSSA  Predicted DSSA\n",
+              "224      41575.0    40659.793050\n",
+              "225      42150.0    40667.242841\n",
+              "226      41250.0    40418.856358\n",
+              "227      42000.0    40047.592764\n",
+              "228      42000.0    40918.596169"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4032b415-bd0b-41ad-b312-7248ee3601f2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual DSSA</th>\n",
+              "      <th>Predicted DSSA</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>41575.0</td>\n",
+              "      <td>40659.793050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>42150.0</td>\n",
+              "      <td>40667.242841</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>41250.0</td>\n",
+              "      <td>40418.856358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>40047.592764</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>40918.596169</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4032b415-bd0b-41ad-b312-7248ee3601f2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4032b415-bd0b-41ad-b312-7248ee3601f2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4032b415-bd0b-41ad-b312-7248ee3601f2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-be2bd1a2-e90c-4400-976c-4a0ad78e49bf\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be2bd1a2-e90c-4400-976c-4a0ad78e49bf')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-be2bd1a2-e90c-4400-976c-4a0ad78e49bf button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df_pred_dssa\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.6593618842817,\n        \"min\": 41250.0,\n        \"max\": 42150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42150.0,\n          42000.0,\n          41575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 328.24824873598226,\n        \"min\": 40047.5927644968,\n        \"max\": 40918.59616935253,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40667.24284052849,\n          40918.59616935253,\n          40418.856358230114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save predictions to CSV files\n",
+        "df_pred_adro.to_csv('GRU_adro_predictions.csv', index=False)\n",
+        "df_pred_dssa.to_csv('GRU_dssa_predictions.csv', index=False)\n",
+        "\n",
+        "print(\"ADRO predictions saved to 'GRU_adro_predictions.csv'\")\n",
+        "print(\"DSSA predictions saved to 'GRU_dssa_predictions.csv'\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kFgxQOW_omsD",
+        "outputId": "4ddb8a8d-6e59-4a6a-cb8e-aef07f7a880a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ADRO predictions saved to 'GRU_adro_predictions.csv'\n",
+            "DSSA predictions saved to 'GRU_dssa_predictions.csv'\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: downloadkan csv adro dan dssa tersebut\n",
+        "\n",
+        "from google.colab import files\n",
+        "\n",
+        "files.download('GRU_adro_predictions.csv')\n",
+        "files.download('GRU_dssa_predictions.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "wFJVLB4uosUR",
+        "outputId": "8a40c11e-103d-4db8-b21a-559a157fa774"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_345c39d5-a51d-4098-9039-50ed7df638a6\", \"GRU_adro_predictions.csv\", 5858)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_a66e346e-750c-4861-a876-9c66055921f5\", \"GRU_dssa_predictions.csv\", 6020)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "M0wbM1VWHRGH"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Graph.ipynb b/Graph.ipynb
new file mode 100644
index 0000000..2857e94
--- /dev/null
+++ b/Graph.ipynb
@@ -0,0 +1,2599 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8URuJbbuo2AR"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ADRO"
+      ],
+      "metadata": {
+        "id": "fn-G0koQqR7G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lstm_adro = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/LSTM_adro_predictions.csv\"\n",
+        "lstm_adro = pd.read_csv(lstm_adro)\n",
+        "lstm_adro.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "y_a5DZnfpJkI",
+        "outputId": "102d13a7-db8a-46cc-9c94-de1731570850"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual ADRO  Predicted ADRO\n",
+              "224       3740.0     3704.371686\n",
+              "225       3760.0     3725.714683\n",
+              "226       3760.0     3753.182715\n",
+              "227       3780.0     3747.377368\n",
+              "228       3940.0     3775.836353"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-af95bb82-c69a-4fd9-9093-a9420315ab03\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual ADRO</th>\n",
+              "      <th>Predicted ADRO</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>3740.0</td>\n",
+              "      <td>3704.371686</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3725.714683</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3753.182715</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>3780.0</td>\n",
+              "      <td>3747.377368</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>3940.0</td>\n",
+              "      <td>3775.836353</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af95bb82-c69a-4fd9-9093-a9420315ab03')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-af95bb82-c69a-4fd9-9093-a9420315ab03 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-af95bb82-c69a-4fd9-9093-a9420315ab03');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-539e7869-c94f-4268-a35f-a552975497b5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-539e7869-c94f-4268-a35f-a552975497b5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-539e7869-c94f-4268-a35f-a552975497b5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"lstm_adro\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.7312669668102,\n        \"min\": 3740.0,\n        \"max\": 3940.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3760.0,\n          3940.0,\n          3740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.283215707097145,\n        \"min\": 3704.371685743332,\n        \"max\": 3775.836352944374,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3725.714682996273,\n          3775.836352944374,\n          3753.182715296745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gru_adro = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/GRU_adro_predictions.csv\"\n",
+        "gru_adro = pd.read_csv(gru_adro)\n",
+        "gru_adro.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "hVbYMUlXpq-X",
+        "outputId": "a2330ac6-2dd6-4d6d-c7e7-d144d5508c58"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual ADRO  Predicted ADRO\n",
+              "224       3740.0     3844.406319\n",
+              "225       3760.0     3855.139246\n",
+              "226       3760.0     3886.059799\n",
+              "227       3780.0     3852.010856\n",
+              "228       3940.0     3923.744762"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-766eabb3-7662-435d-bed3-ddbaceb72b88\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual ADRO</th>\n",
+              "      <th>Predicted ADRO</th>\n",
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+              "      <td>3760.0</td>\n",
+              "      <td>3886.059799</td>\n",
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+              "      <td>3940.0</td>\n",
+              "      <td>3923.744762</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-766eabb3-7662-435d-bed3-ddbaceb72b88')\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-766eabb3-7662-435d-bed3-ddbaceb72b88 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-766eabb3-7662-435d-bed3-ddbaceb72b88');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-23c4adca-ab77-4719-ae3b-13b133f0edc6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23c4adca-ab77-4719-ae3b-13b133f0edc6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-23c4adca-ab77-4719-ae3b-13b133f0edc6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"gru_adro\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.7312669668102,\n        \"min\": 3740.0,\n        \"max\": 3940.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3760.0,\n          3940.0,\n          3740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.86369565366565,\n        \"min\": 3844.406318962574,\n        \"max\": 3923.744761586189,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3855.1392456889153,\n          3923.744761586189,\n          3886.0597985982895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gru_adro = gru_adro.drop(columns=[\"Actual ADRO\"])\n",
+        "gru_adro.rename(columns={'Predicted ADRO': 'Predicted ADRO GRU'}, inplace=True)"
+      ],
+      "metadata": {
+        "id": "aw83tVf7ELRJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "merged_adro = pd.concat([lstm_adro, gru_adro], axis=1)\n",
+        "merged_adro.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "nZmHCxC_p1wc",
+        "outputId": "fb43a8aa-8878-4b53-b126-3a09a7f03b97"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual ADRO  Predicted ADRO  Predicted ADRO GRU\n",
+              "224       3740.0     3704.371686         3844.406319\n",
+              "225       3760.0     3725.714683         3855.139246\n",
+              "226       3760.0     3753.182715         3886.059799\n",
+              "227       3780.0     3747.377368         3852.010856\n",
+              "228       3940.0     3775.836353         3923.744762"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-051c5a9a-dde2-4c60-a20e-f4e58bef99d8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "</style>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual ADRO</th>\n",
+              "      <th>Predicted ADRO</th>\n",
+              "      <th>Predicted ADRO GRU</th>\n",
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+              "      <td>3940.0</td>\n",
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+              "\n",
+              "  <style>\n",
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+              "      display:flex;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-051c5a9a-dde2-4c60-a20e-f4e58bef99d8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-051c5a9a-dde2-4c60-a20e-f4e58bef99d8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9033fca9-81ee-438e-94a4-dc31863f9ff8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9033fca9-81ee-438e-94a4-dc31863f9ff8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9033fca9-81ee-438e-94a4-dc31863f9ff8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"merged_adro\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.7312669668102,\n        \"min\": 3740.0,\n        \"max\": 3940.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3760.0,\n          3940.0,\n          3740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.283215707097145,\n        \"min\": 3704.371685743332,\n        \"max\": 3775.836352944374,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3725.714682996273,\n          3775.836352944374,\n          3753.182715296745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO GRU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.86369565366565,\n        \"min\": 3844.406318962574,\n        \"max\": 3923.744761586189,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3855.1392456889153,\n          3923.744761586189,\n          3886.0597985982895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# DSSA"
+      ],
+      "metadata": {
+        "id": "OFxcS-3hqUaj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lstm_dssa = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/LSTM_dssa_predictions.csv\"\n",
+        "lstm_dssa = pd.read_csv(lstm_dssa)\n",
+        "lstm_dssa.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "H0JC6IFipO3e",
+        "outputId": "3f124942-2723-4a84-dd6d-4a40aaedafe7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual DSSA  Predicted DSSA\n",
+              "224      41575.0    38529.780222\n",
+              "225      42150.0    39018.766194\n",
+              "226      41250.0    39050.567652\n",
+              "227      42000.0    38706.559837\n",
+              "228      42000.0    39096.267545"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dd3efcb4-9022-4127-99b5-aea2ccd5e781\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual DSSA</th>\n",
+              "      <th>Predicted DSSA</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>41575.0</td>\n",
+              "      <td>38529.780222</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>42150.0</td>\n",
+              "      <td>39018.766194</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>41250.0</td>\n",
+              "      <td>39050.567652</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>38706.559837</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>39096.267545</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd3efcb4-9022-4127-99b5-aea2ccd5e781')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-dd3efcb4-9022-4127-99b5-aea2ccd5e781 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-dd3efcb4-9022-4127-99b5-aea2ccd5e781');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6d902d37-eddd-4493-838d-d9418143ff4d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d902d37-eddd-4493-838d-d9418143ff4d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6d902d37-eddd-4493-838d-d9418143ff4d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"lstm_dssa\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.6593618842817,\n        \"min\": 41250.0,\n        \"max\": 42150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42150.0,\n          42000.0,\n          41575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248.9255214799381,\n        \"min\": 38529.78022158146,\n        \"max\": 39096.26754462719,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          39018.76619428396,\n          39096.26754462719,\n          39050.5676522851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gru_dssa = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/GRU_dssa_predictions.csv\"\n",
+        "gru_dssa = pd.read_csv(gru_dssa)\n",
+        "gru_dssa.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "LGL6JSpApo2k",
+        "outputId": "7a440319-1c1a-418f-bbaa-bdc80160092a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual DSSA  Predicted DSSA\n",
+              "224      41575.0    40659.793050\n",
+              "225      42150.0    40667.242841\n",
+              "226      41250.0    40418.856358\n",
+              "227      42000.0    40047.592764\n",
+              "228      42000.0    40918.596169"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-43d99891-d985-4e15-98ca-b68cdfc71724\" class=\"colab-df-container\">\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual DSSA</th>\n",
+              "      <th>Predicted DSSA</th>\n",
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+              "      <td>40667.242841</td>\n",
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+              "      <td>41250.0</td>\n",
+              "      <td>40418.856358</td>\n",
+              "    </tr>\n",
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+              "    </tr>\n",
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+              "      <td>42000.0</td>\n",
+              "      <td>40918.596169</td>\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-43d99891-d985-4e15-98ca-b68cdfc71724');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "  }\n",
+              "\n",
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+              "    fill: var(--disabled-fill-color);\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
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+              "</style>\n",
+              "\n",
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+              "        async function quickchart(key) {\n",
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+              "            document.querySelector('#' + key + ' button');\n",
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+              "            const charts = await google.colab.kernel.invokeFunction(\n",
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+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"gru_dssa\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.6593618842817,\n        \"min\": 41250.0,\n        \"max\": 42150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42150.0,\n          42000.0,\n          41575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 328.24824873598226,\n        \"min\": 40047.5927644968,\n        \"max\": 40918.59616935253,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40667.24284052849,\n          40918.59616935253,\n          40418.856358230114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gru_dssa = gru_dssa.drop(columns=[\"Actual DSSA\"])\n",
+        "gru_dssa.rename(columns={'Predicted DSSA': 'Predicted DSSA GRU'}, inplace=True)"
+      ],
+      "metadata": {
+        "id": "0N4znkULuWqH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "merged_dssa = pd.concat([lstm_dssa, gru_dssa], axis=1)\n",
+        "merged_dssa.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pbfR3PdMqXYr",
+        "outputId": "942c9e92-6144-403b-8264-45a65179f734"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual DSSA  Predicted DSSA  Predicted DSSA GRU\n",
+              "224      41575.0    38529.780222        40659.793050\n",
+              "225      42150.0    39018.766194        40667.242841\n",
+              "226      41250.0    39050.567652        40418.856358\n",
+              "227      42000.0    38706.559837        40047.592764\n",
+              "228      42000.0    39096.267545        40918.596169"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-335fb64f-2c17-49a7-87a4-579b33dadb12\" class=\"colab-df-container\">\n",
+              "    <div>\n",
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+              "      <th></th>\n",
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+              "      <th>Predicted DSSA</th>\n",
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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-335fb64f-2c17-49a7-87a4-579b33dadb12 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-335fb64f-2c17-49a7-87a4-579b33dadb12');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9f80e4c7-a196-46ed-a5f0-3e923c843680\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f80e4c7-a196-46ed-a5f0-3e923c843680')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9f80e4c7-a196-46ed-a5f0-3e923c843680 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"merged_dssa\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.6593618842817,\n        \"min\": 41250.0,\n        \"max\": 42150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42150.0,\n          42000.0,\n          41575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248.9255214799381,\n        \"min\": 38529.78022158146,\n        \"max\": 39096.26754462719,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          39018.76619428396,\n          39096.26754462719,\n          39050.5676522851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA GRU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 328.24824873598226,\n        \"min\": 40047.5927644968,\n        \"max\": 40918.59616935253,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40667.24284052849,\n          40918.59616935253,\n          40418.856358230114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plot the predicted vs actual values"
+      ],
+      "metadata": {
+        "id": "1OD-IVt3qpKD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "merged_adro.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "bpxLr6NjsNuc",
+        "outputId": "0ce40ea5-2ec5-411c-bfa8-bea82645dcb8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual ADRO  Predicted ADRO  Predicted ADRO GRU\n",
+              "224       3740.0     3704.371686         3844.406319\n",
+              "225       3760.0     3725.714683         3855.139246\n",
+              "226       3760.0     3753.182715         3886.059799\n",
+              "227       3780.0     3747.377368         3852.010856\n",
+              "228       3940.0     3775.836353         3923.744762"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ba7ca38f-ac0a-4120-817b-d24b6319c257\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual ADRO</th>\n",
+              "      <th>Predicted ADRO</th>\n",
+              "      <th>Predicted ADRO GRU</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>3740.0</td>\n",
+              "      <td>3704.371686</td>\n",
+              "      <td>3844.406319</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3725.714683</td>\n",
+              "      <td>3855.139246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>3760.0</td>\n",
+              "      <td>3753.182715</td>\n",
+              "      <td>3886.059799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>3780.0</td>\n",
+              "      <td>3747.377368</td>\n",
+              "      <td>3852.010856</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>3940.0</td>\n",
+              "      <td>3775.836353</td>\n",
+              "      <td>3923.744762</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba7ca38f-ac0a-4120-817b-d24b6319c257')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ba7ca38f-ac0a-4120-817b-d24b6319c257 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ba7ca38f-ac0a-4120-817b-d24b6319c257');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a15e1346-b8f7-435d-8d96-aa9bc312c0e9\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a15e1346-b8f7-435d-8d96-aa9bc312c0e9')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a15e1346-b8f7-435d-8d96-aa9bc312c0e9 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"merged_adro\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.7312669668102,\n        \"min\": 3740.0,\n        \"max\": 3940.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3760.0,\n          3940.0,\n          3740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.283215707097145,\n        \"min\": 3704.371685743332,\n        \"max\": 3775.836352944374,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3725.714682996273,\n          3775.836352944374,\n          3753.182715296745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted ADRO GRU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.86369565366565,\n        \"min\": 3844.406318962574,\n        \"max\": 3923.744761586189,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3855.1392456889153,\n          3923.744761586189,\n          3886.0597985982895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "merged_dssa.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "IhN8Cxu0q4sk",
+        "outputId": "33759dd0-f8f9-4c31-b2e7-136a07f9b4e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Actual DSSA  Predicted DSSA  Predicted DSSA GRU\n",
+              "224      41575.0    38529.780222        40659.793050\n",
+              "225      42150.0    39018.766194        40667.242841\n",
+              "226      41250.0    39050.567652        40418.856358\n",
+              "227      42000.0    38706.559837        40047.592764\n",
+              "228      42000.0    39096.267545        40918.596169"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-711bc76f-8b67-46cc-a8be-2e990f3f0f8a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual DSSA</th>\n",
+              "      <th>Predicted DSSA</th>\n",
+              "      <th>Predicted DSSA GRU</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>41575.0</td>\n",
+              "      <td>38529.780222</td>\n",
+              "      <td>40659.793050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>42150.0</td>\n",
+              "      <td>39018.766194</td>\n",
+              "      <td>40667.242841</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>41250.0</td>\n",
+              "      <td>39050.567652</td>\n",
+              "      <td>40418.856358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>38706.559837</td>\n",
+              "      <td>40047.592764</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>42000.0</td>\n",
+              "      <td>39096.267545</td>\n",
+              "      <td>40918.596169</td>\n",
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+              "  </tbody>\n",
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+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-711bc76f-8b67-46cc-a8be-2e990f3f0f8a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-711bc76f-8b67-46cc-a8be-2e990f3f0f8a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-711bc76f-8b67-46cc-a8be-2e990f3f0f8a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1069cc25-536f-4a11-8e08-955154d2f2b8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1069cc25-536f-4a11-8e08-955154d2f2b8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1069cc25-536f-4a11-8e08-955154d2f2b8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"merged_dssa\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Actual DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.6593618842817,\n        \"min\": 41250.0,\n        \"max\": 42150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42150.0,\n          42000.0,\n          41575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248.9255214799381,\n        \"min\": 38529.78022158146,\n        \"max\": 39096.26754462719,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          39018.76619428396,\n          39096.26754462719,\n          39050.5676522851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted DSSA GRU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 328.24824873598226,\n        \"min\": 40047.5927644968,\n        \"max\": 40918.59616935253,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40667.24284052849,\n          40918.59616935253,\n          40418.856358230114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(16, 4), facecolor=\"#f8f4f4\")\n",
+        "plt.plot(merged_adro['Actual ADRO'], label='Actual')\n",
+        "plt.plot(merged_adro['Predicted ADRO'], label='LSTM Predicted')\n",
+        "plt.plot(merged_adro['Predicted ADRO GRU'], label='GRU Predicted')\n",
+        "plt.title(\"Prediction for ADRO.JK\")\n",
+        "plt.legend()\n",
+        "plt.grid(True, linestyle=\"--\", linewidth=0.5)\n",
+        "plt.gca().set_facecolor(\"#f8f4f4\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 306
+        },
+        "id": "XXVIi4Vkvzyg",
+        "outputId": "e0c06861-4598-4d2d-feb3-2cde23745b44"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(16, 4), facecolor=\"#f8f4f4\")\n",
+        "plt.plot(merged_dssa['Actual DSSA'], label='Actual')\n",
+        "plt.plot(merged_dssa['Predicted DSSA'], label='LSTM Predicted')\n",
+        "plt.plot(merged_dssa['Predicted DSSA GRU'], label='GRU Predicted')\n",
+        "plt.title(\"Prediction for DSSA.JK\")\n",
+        "plt.legend()\n",
+        "plt.grid(True, linestyle=\"--\", linewidth=0.5)\n",
+        "plt.gca().set_facecolor(\"#f8f4f4\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 306
+        },
+        "id": "RCp9mMCfwDnO",
+        "outputId": "4a3db2a3-e2e8-47e9-eebb-d99ac6f91a68"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Aabs43nfEUVc"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/LSTM.ipynb b/LSTM.ipynb
new file mode 100644
index 0000000..8e3a545
--- /dev/null
+++ b/LSTM.ipynb
@@ -0,0 +1,3611 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import Libraries"
+      ],
+      "metadata": {
+        "id": "XTBQCgfi4s_3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "13IWG7jegtHo"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import missingno as msno\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
+        "from tensorflow.keras.callbacks import EarlyStopping\n",
+        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Reading Dataset"
+      ],
+      "metadata": {
+        "id": "_XF8Df9v4v3C"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ADRO"
+      ],
+      "metadata": {
+        "id": "f30_aaa44yhE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_adro = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Adaro%20Energy%20Indonesia%20Tbk_2020-2024.csv\"\n",
+        "adro = pd.read_csv(url_adro)\n",
+        "adro.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "CJ69vEFiDxO3",
+        "outputId": "2614419c-d979-422c-b6e2-b129a6c64e90"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close   Adj Close     Volume\n",
+              "0  2020-01-02  1555.0  1555.0  1490.0  1495.0  835.632202   76612000\n",
+              "1  2020-01-03  1460.0  1470.0  1425.0  1465.0  856.372375  117795600\n",
+              "2  2020-01-06  1455.0  1515.0  1450.0  1465.0  856.372375   61423200\n",
+              "3  2020-01-07  1480.0  1540.0  1460.0  1540.0  900.213867   74336500\n",
+              "4  2020-01-08  1530.0  1535.0  1505.0  1505.0  879.754395   55121100"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c004e24d-e22b-4d48-a782-507a8898c7c6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1490.0</td>\n",
+              "      <td>1495.0</td>\n",
+              "      <td>835.632202</td>\n",
+              "      <td>76612000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1470.0</td>\n",
+              "      <td>1425.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>117795600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1455.0</td>\n",
+              "      <td>1515.0</td>\n",
+              "      <td>1450.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "      <td>61423200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1480.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>900.213867</td>\n",
+              "      <td>74336500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1530.0</td>\n",
+              "      <td>1535.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>879.754395</td>\n",
+              "      <td>55121100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c004e24d-e22b-4d48-a782-507a8898c7c6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c004e24d-e22b-4d48-a782-507a8898c7c6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c004e24d-e22b-4d48-a782-507a8898c7c6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7741bfef-16db-438f-8320-5159050a7662\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7741bfef-16db-438f-8320-5159050a7662')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7741bfef-16db-438f-8320-5159050a7662 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro",
+              "summary": "{\n  \"name\": \"adro\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 913.246493337149,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          3120.0,\n          1670.0,\n          3090.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 924.8420313199422,\n        \"min\": 670.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          3020.0,\n          1930.0,\n          3320.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 902.619092508634,\n        \"min\": 610.0,\n        \"max\": 4080.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          2870.0,\n          1900.0,\n          3120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 914.3952655818309,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          3290.0,\n          775.0,\n          3640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 865.5150705162999,\n        \"min\": 377.03759765625,\n        \"max\": 3910.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          2913.447509765625,\n          2624.987548828125,\n          776.3223266601562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68712203,\n        \"min\": 0,\n        \"max\": 539995200,\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          57485800,\n          121344700,\n          40850500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B7p8nU8okoXL",
+        "outputId": "5747ace4-8aac-4686-8030-6cac5417d3d5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DSSA"
+      ],
+      "metadata": {
+        "id": "PTWSX4dM5fsn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url_dssa = \"https://raw.githubusercontent.com/MIlhamEr/Skripsi/refs/heads/main/dataset/PT%20Dian%20Swastatika%20Sentosa%20Tbk_2020-2024.csv\"\n",
+        "dssa = pd.read_csv(url_dssa)\n",
+        "dssa.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "opJl-3DOFICJ",
+        "outputId": "191da2e1-68bf-4417-b2bd-858f120e8a28"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date    Open    High     Low   Close  Adj Close  Volume\n",
+              "0  2020-01-02  1360.0  1435.0  1360.0  1435.0     1435.0    5000\n",
+              "1  2020-01-03  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "2  2020-01-06  1437.5  1437.5  1435.0  1435.0     1435.0    3000\n",
+              "3  2020-01-07  1435.0  1435.0  1435.0  1435.0     1435.0       0\n",
+              "4  2020-01-08  1435.0  1435.0  1435.0  1435.0     1435.0       0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-512104df-1b6a-442f-aab0-5e89c87396d7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1360.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020-01-06</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1437.5</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>3000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>1435.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-512104df-1b6a-442f-aab0-5e89c87396d7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-512104df-1b6a-442f-aab0-5e89c87396d7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-512104df-1b6a-442f-aab0-5e89c87396d7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6c1c60f1-0c70-4492-85c4-f527f74e8624\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c1c60f1-0c70-4492-85c4-f527f74e8624')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6c1c60f1-0c70-4492-85c4-f527f74e8624 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dssa",
+              "summary": "{\n  \"name\": \"dssa\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1148,\n        \"samples\": [\n          \"2020-09-14\",\n          \"2022-03-11\",\n          \"2023-07-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8180.650972978378,\n        \"min\": 910.0,\n        \"max\": 42350.0,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          28700.0,\n          2227.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8370.34990700453,\n        \"min\": 920.0,\n        \"max\": 44775.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          4270.0,\n          1620.0,\n          13050.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8089.417253892426,\n        \"min\": 910.0,\n        \"max\": 41550.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          3815.0,\n          2087.5,\n          2125.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8228.663218599353,\n        \"min\": 920.0,\n        \"max\": 41875.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          1550.0,\n          3392.5,\n          4600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 510893,\n        \"min\": 0,\n        \"max\": 12000000,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          1529000,\n          1100200,\n          1728600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa.info()"
+      ],
+      "metadata": {
+        "id": "4l_a9qfpoCar",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d182956f-34d6-4b8e-e9c2-b387b6cbb2b2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1148 entries, 0 to 1147\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       1148 non-null   object \n",
+            " 1   Open       1148 non-null   float64\n",
+            " 2   High       1148 non-null   float64\n",
+            " 3   Low        1148 non-null   float64\n",
+            " 4   Close      1148 non-null   float64\n",
+            " 5   Adj Close  1148 non-null   float64\n",
+            " 6   Volume     1148 non-null   int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 62.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Pre-processing Data"
+      ],
+      "metadata": {
+        "id": "GiaxLakO6sVo"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Normalisasi Data"
+      ],
+      "metadata": {
+        "id": "scobtTMV6p5p"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = MinMaxScaler()"
+      ],
+      "metadata": {
+        "id": "AEAlTru7kzRs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def normalize_data(df):\n",
+        "    scaled_df = pd.DataFrame(scaler.fit_transform(df.iloc[:, 1:]), columns=df.columns[1:], index=df.index)\n",
+        "    return scaled_df"
+      ],
+      "metadata": {
+        "id": "sKSDpg6E9BC8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_norm = adro.copy()\n",
+        "adro_norm = adro_norm.drop('Volume', axis=1)\n",
+        "adro_norm = normalize_data(adro_norm)\n",
+        "adro_norm.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "SIftpE5Z5_Rh",
+        "outputId": "a2fe2b06-bc87-4d6d-d4b4-e0ade5d28713"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Open      High       Low     Close  Adj Close\n",
+              "0  0.260372  0.247207  0.253602  0.243205   0.129805\n",
+              "1  0.233190  0.223464  0.234870  0.234621   0.135675\n",
+              "2  0.231760  0.236034  0.242075  0.234621   0.135675\n",
+              "3  0.238913  0.243017  0.244957  0.256080   0.148084\n",
+              "4  0.253219  0.241620  0.257925  0.246066   0.142293"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9b18945e-6380-4025-a354-70f27df2b4d9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.260372</td>\n",
+              "      <td>0.247207</td>\n",
+              "      <td>0.253602</td>\n",
+              "      <td>0.243205</td>\n",
+              "      <td>0.129805</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.233190</td>\n",
+              "      <td>0.223464</td>\n",
+              "      <td>0.234870</td>\n",
+              "      <td>0.234621</td>\n",
+              "      <td>0.135675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.231760</td>\n",
+              "      <td>0.236034</td>\n",
+              "      <td>0.242075</td>\n",
+              "      <td>0.234621</td>\n",
+              "      <td>0.135675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.238913</td>\n",
+              "      <td>0.243017</td>\n",
+              "      <td>0.244957</td>\n",
+              "      <td>0.256080</td>\n",
+              "      <td>0.148084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.253219</td>\n",
+              "      <td>0.241620</td>\n",
+              "      <td>0.257925</td>\n",
+              "      <td>0.246066</td>\n",
+              "      <td>0.142293</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b18945e-6380-4025-a354-70f27df2b4d9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9b18945e-6380-4025-a354-70f27df2b4d9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9b18945e-6380-4025-a354-70f27df2b4d9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6ef8e83d-6b7c-45a8-8b5e-126612b0db38\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ef8e83d-6b7c-45a8-8b5e-126612b0db38')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6ef8e83d-6b7c-45a8-8b5e-126612b0db38 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro_norm",
+              "summary": "{\n  \"name\": \"adro_norm\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2613008564627031,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          0.7081545064377683,\n          0.2932761087267525,\n          0.6995708154506439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2583357629385311,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          0.6564245810055865,\n          0.35195530726256974,\n          0.7402234636871508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2601207759390876,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          0.6512968299711817,\n          0.3717579250720462,\n          0.723342939481268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26162954666146787,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          0.7567954220314737,\n          0.037195994277539335,\n          0.8569384835479257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24498281383977363,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          0.7179272302549082,\n          0.6362790472042941,\n          0.11301697655741333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro = adro.drop('Volume', axis=1)\n",
+        "adro = adro.drop('Date', axis=1)\n",
+        "adro.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "w2qE1QRdPBvi",
+        "outputId": "8abda8c3-d4f9-446f-d049-074dcdabaf4a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Open    High     Low   Close   Adj Close\n",
+              "0  1555.0  1555.0  1490.0  1495.0  835.632202\n",
+              "1  1460.0  1470.0  1425.0  1465.0  856.372375\n",
+              "2  1455.0  1515.0  1450.0  1465.0  856.372375\n",
+              "3  1480.0  1540.0  1460.0  1540.0  900.213867\n",
+              "4  1530.0  1535.0  1505.0  1505.0  879.754395"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-be6e6530-fa48-4b0d-9e5f-3565d1587b85\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1555.0</td>\n",
+              "      <td>1490.0</td>\n",
+              "      <td>1495.0</td>\n",
+              "      <td>835.632202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1470.0</td>\n",
+              "      <td>1425.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1455.0</td>\n",
+              "      <td>1515.0</td>\n",
+              "      <td>1450.0</td>\n",
+              "      <td>1465.0</td>\n",
+              "      <td>856.372375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1480.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>1460.0</td>\n",
+              "      <td>1540.0</td>\n",
+              "      <td>900.213867</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1530.0</td>\n",
+              "      <td>1535.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>1505.0</td>\n",
+              "      <td>879.754395</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be6e6530-fa48-4b0d-9e5f-3565d1587b85')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-be6e6530-fa48-4b0d-9e5f-3565d1587b85 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-be6e6530-fa48-4b0d-9e5f-3565d1587b85');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c4570e13-3f5c-4b83-83be-4783729c8adf\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4570e13-3f5c-4b83-83be-4783729c8adf')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c4570e13-3f5c-4b83-83be-4783729c8adf button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "adro",
+              "summary": "{\n  \"name\": \"adro\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 913.246493337149,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          3120.0,\n          1670.0,\n          3090.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 924.8420313199422,\n        \"min\": 670.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          3020.0,\n          1930.0,\n          3320.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 902.619092508634,\n        \"min\": 610.0,\n        \"max\": 4080.0,\n        \"num_unique_values\": 345,\n        \"samples\": [\n          2870.0,\n          1900.0,\n          3120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 914.3952655818309,\n        \"min\": 645.0,\n        \"max\": 4140.0,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          3290.0,\n          775.0,\n          3640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 865.5150705162999,\n        \"min\": 377.03759765625,\n        \"max\": 3910.0,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          2913.447509765625,\n          2624.987548828125,\n          776.3223266601562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dssa_norm = dssa.copy()\n",
+        "dssa_norm = dssa_norm.drop('Volume', axis=1)\n",
+        "dssa_norm = normalize_data(dssa_norm)\n",
+        "dssa_norm.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "bFqa6kGj6Bn4",
+        "outputId": "8ea4e3e0-1d0e-4a9e-e10e-e4c5a529e575"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Open      High       Low     Close  Adj Close\n",
+              "0  0.010859  0.011743  0.011073  0.012575   0.012575\n",
+              "1  0.012669  0.011743  0.012918  0.012575   0.012575\n",
+              "2  0.012729  0.011800  0.012918  0.012575   0.012575\n",
+              "3  0.012669  0.011743  0.012918  0.012575   0.012575\n",
+              "4  0.012669  0.011743  0.012918  0.012575   0.012575"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9d00ed60-2204-4efb-99c5-f84dafff9a4e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.010859</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.011073</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.012729</td>\n",
+              "      <td>0.011800</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.012669</td>\n",
+              "      <td>0.011743</td>\n",
+              "      <td>0.012918</td>\n",
+              "      <td>0.012575</td>\n",
+              "      <td>0.012575</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d00ed60-2204-4efb-99c5-f84dafff9a4e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9d00ed60-2204-4efb-99c5-f84dafff9a4e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9d00ed60-2204-4efb-99c5-f84dafff9a4e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-26078ec6-fd7c-4e59-bc3f-20d0e77f1764\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26078ec6-fd7c-4e59-bc3f-20d0e77f1764')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-26078ec6-fd7c-4e59-bc3f-20d0e77f1764 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dssa_norm",
+              "summary": "{\n  \"name\": \"dssa_norm\",\n  \"rows\": 1148,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19740953120121557,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          0.6706081081081081,\n          0.03179295366795366,\n          0.02931949806949807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1908642094859088,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          0.07638809713829667,\n          0.01596169193934557,\n          0.27659331889180255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19905062140483315,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          0.07148129921259842,\n          0.028973917322834646,\n          0.029896653543307086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20091962443167757,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          0.015382737150531074,\n          0.06037113905506044,\n          0.08985471859357833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20091962443167757,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 330,\n        \"samples\": [\n          0.015382737150531074,\n          0.06037113905506044,\n          0.08985471859357833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Windowing (Time Series Data)"
+      ],
+      "metadata": {
+        "id": "Mt0Pcgzwu5Zs"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Bagian Baru"
+      ],
+      "metadata": {
+        "id": "w_3dti2a6Ntc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data look back yaitu adalah data yang biasanya dipakai untuk tipe time series, data ini menjadikan nilai lookback+1 sebagai nilai prediksi , dan data lookbacknya sebagai fitur.Ini berlaku untuk seluruh data, contoh penggambarannya seperti pada gambar diatas"
+      ],
+      "metadata": {
+        "id": "J-_ZgIAgznVd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def prepare_data(df, time_step):\n",
+        "    X, y = [], []\n",
+        "    for i in range(len(df)-time_step):\n",
+        "        t = []\n",
+        "        for j in range(time_step):\n",
+        "            t.append(df.iloc[i + j].values)  # Use all columns for features\n",
+        "        X.append(t)\n",
+        "        y.append(df['High'][i + time_step]) # Predict High price\n",
+        "    return np.array(X), np.array(y)"
+      ],
+      "metadata": {
+        "id": "SGATnJPNudrF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "time_step = 7\n",
+        "X_adro, y_adro = prepare_data(adro_norm, time_step)"
+      ],
+      "metadata": {
+        "id": "FKhvyz5WugaA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X_adro[1])\n",
+        "print(y_adro[1])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3BVbHH-ew4dm",
+        "outputId": "55160db0-8caf-4116-f6d5-79e5565c8cab"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0.23319027 0.22346369 0.23487032 0.23462089 0.13567503]\n",
+            " [0.23175966 0.23603352 0.24207493 0.23462089 0.13567503]\n",
+            " [0.23891273 0.24301676 0.24495677 0.25608011 0.1480843 ]\n",
+            " [0.25321888 0.24162011 0.25792507 0.24606581 0.14229328]\n",
+            " [0.25035765 0.23882682 0.24927954 0.24320458 0.14063873]\n",
+            " [0.25035765 0.25       0.26080692 0.25894134 0.14973887]\n",
+            " [0.26895565 0.25837989 0.27233429 0.26323319 0.15222076]]\n",
+            "0.2555865921787709\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_adro.shape, y_adro.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C_07IBA047Yh",
+        "outputId": "8dec3480-829c-4b35-efd3-329903c951d0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((1141, 7, 5), (1141,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_dssa, y_dssa = prepare_data(dssa_norm, time_step)"
+      ],
+      "metadata": {
+        "id": "9MawPQRzul5d"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Split data into training and testing sets"
+      ],
+      "metadata": {
+        "id": "pTzWLSzTu9gX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_adro, X_test_adro, y_train_adro, y_test_adro = train_test_split(X_adro, y_adro, test_size=0.2, random_state=42, shuffle=False)\n",
+        "X_train_dssa, X_test_dssa, y_train_dssa, y_test_dssa = train_test_split(X_dssa, y_dssa, test_size=0.2, random_state=42, shuffle=False)"
+      ],
+      "metadata": {
+        "id": "f3t130E5uoI1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_adro.shape, X_test_adro.shape, y_train_adro.shape, y_test_adro.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xNNB6-e9e3V9",
+        "outputId": "e784540a-96b7-44cc-a519-5b02fa6fa012"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((912, 7, 5), (229, 7, 5), (912,), (229,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Mining"
+      ],
+      "metadata": {
+        "id": "n0BKrKtoo8Cb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_lstm_model(units):\n",
+        "    model = Sequential()\n",
+        "    model.add(LSTM(units=units, return_sequences=True, input_shape=(X_train_adro.shape[1], X_train_adro.shape[2])))\n",
+        "    model.add(Dropout(0.2))\n",
+        "    model.add(LSTM(units=units, return_sequences=False))\n",
+        "    model.add(Dropout(0.2))\n",
+        "    model.add(Dense(units=1))  # Output layer for single prediction (predicted stock price)\n",
+        "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
+        "    return model"
+      ],
+      "metadata": {
+        "id": "bProaTPtvI4l"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Bobot LSTM"
+      ],
+      "metadata": {
+        "id": "rIEZVKoRtq8H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create model\n",
+        "lstm_model = create_lstm_model(50)  # Example: 50 LSTM units"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aT5t4Cq0aano",
+        "outputId": "a17dd5b2-db0a-40ac-e115-4df8a11439aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "layer = lstm_model.layers[0]  # Ambil hanya layer pertama\n",
+        "if isinstance(layer, LSTM):\n",
+        "    weights = layer.get_weights()\n",
+        "    # W: Input weights, U: Recurrent weights, b: Bias\n",
+        "    W, U, b = weights[0], weights[1], weights[2]\n",
+        "    units = U.shape[0]  # Jumlah unit LSTM\n",
+        "    columns = [\"Open\", \"High\", \"Low\", \"Close\", \"Adj Close\"]\n",
+        "    num_features = len(columns)\n",
+        "\n",
+        "    # Memisahkan bobot untuk setiap gate\n",
+        "    W_f, W_i, W_c, W_o = np.split(W, 4, axis=1)\n",
+        "    U_f, U_i, U_c, U_o = np.split(U, 4, axis=1)\n",
+        "    b_f, b_i, b_c, b_o = np.split(b, 4, axis=0)\n",
+        "\n",
+        "    print(\"LSTM Layer 1 Weights Analysis:\")\n",
+        "    for i in range(num_features):  # Iterasi sesuai jumlah fitur\n",
+        "        print(f\"Feature: {columns[i]}\")\n",
+        "        print(f\"  Forget Gate W_f[{i}, 0]: {W_f[i, 0]}\")\n",
+        "        print(f\"  Input Gate W_i[{i}, 0]: {W_i[i, 0]}\")\n",
+        "        print(f\"  Cell State W_c[{i}, 0]: {W_c[i, 0]}\")\n",
+        "        print(f\"  Output Gate W_o[{i}, 0]: {W_o[i, 0]}\")\n",
+        "        print(f\"  Forget Gate U_f[{i}, 0]: {U_f[i, 0]}\")\n",
+        "        print(f\"  Input Gate U_i[{i}, 0]: {U_i[i, 0]}\")\n",
+        "        print(f\"  Cell State U_c[{i}, 0]: {U_c[i, 0]}\")\n",
+        "        print(f\"  Output Gate U_o[{i}, 0]: {U_o[i, 0]}\")\n",
+        "        print(f\"  Forget Gate b_f[{i}]: {b_f[i]}\")\n",
+        "        print(f\"  Input Gate b_i[{i}]: {b_i[i]}\")\n",
+        "        print(f\"  Cell State b_c[{i}]: {b_c[i]}\")\n",
+        "        print(f\"  Output Gate b_o[{i}]: {b_o[i]}\")\n",
+        "        print(\"====\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WO6adjDEhB6F",
+        "outputId": "ee892ce6-40aa-4bec-f6f8-e212e10eaa99"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "LSTM Layer 1 Weights Analysis:\n",
+            "Feature: Open\n",
+            "  Forget Gate W_f[0, 0]: -0.04256738722324371\n",
+            "  Input Gate W_i[0, 0]: -0.010287865996360779\n",
+            "  Cell State W_c[0, 0]: -0.05227087438106537\n",
+            "  Output Gate W_o[0, 0]: -0.08412709832191467\n",
+            "  Forget Gate U_f[0, 0]: 0.04162132740020752\n",
+            "  Input Gate U_i[0, 0]: -0.012287689372897148\n",
+            "  Cell State U_c[0, 0]: -0.011030183173716068\n",
+            "  Output Gate U_o[0, 0]: 0.03395417705178261\n",
+            "  Forget Gate b_f[0]: 0.0\n",
+            "  Input Gate b_i[0]: 1.0\n",
+            "  Cell State b_c[0]: 0.0\n",
+            "  Output Gate b_o[0]: 0.0\n",
+            "====\n",
+            "Feature: High\n",
+            "  Forget Gate W_f[1, 0]: 0.06724417209625244\n",
+            "  Input Gate W_i[1, 0]: 0.0061102211475372314\n",
+            "  Cell State W_c[1, 0]: 0.16968616843223572\n",
+            "  Output Gate W_o[1, 0]: 0.05730287730693817\n",
+            "  Forget Gate U_f[1, 0]: 0.09096574783325195\n",
+            "  Input Gate U_i[1, 0]: 0.009209300391376019\n",
+            "  Cell State U_c[1, 0]: 0.04923269897699356\n",
+            "  Output Gate U_o[1, 0]: -0.08389423787593842\n",
+            "  Forget Gate b_f[1]: 0.0\n",
+            "  Input Gate b_i[1]: 1.0\n",
+            "  Cell State b_c[1]: 0.0\n",
+            "  Output Gate b_o[1]: 0.0\n",
+            "====\n",
+            "Feature: Low\n",
+            "  Forget Gate W_f[2, 0]: -0.07467328011989594\n",
+            "  Input Gate W_i[2, 0]: 0.07021500170230865\n",
+            "  Cell State W_c[2, 0]: 0.1201578676700592\n",
+            "  Output Gate W_o[2, 0]: -0.13118976354599\n",
+            "  Forget Gate U_f[2, 0]: 0.07825936377048492\n",
+            "  Input Gate U_i[2, 0]: 0.07037021964788437\n",
+            "  Cell State U_c[2, 0]: 0.014703350141644478\n",
+            "  Output Gate U_o[2, 0]: -0.10391176491975784\n",
+            "  Forget Gate b_f[2]: 0.0\n",
+            "  Input Gate b_i[2]: 1.0\n",
+            "  Cell State b_c[2]: 0.0\n",
+            "  Output Gate b_o[2]: 0.0\n",
+            "====\n",
+            "Feature: Close\n",
+            "  Forget Gate W_f[3, 0]: -0.08179786801338196\n",
+            "  Input Gate W_i[3, 0]: 0.1663205325603485\n",
+            "  Cell State W_c[3, 0]: 0.09247386455535889\n",
+            "  Output Gate W_o[3, 0]: 0.09769025444984436\n",
+            "  Forget Gate U_f[3, 0]: 0.10877078026533127\n",
+            "  Input Gate U_i[3, 0]: -0.07621479034423828\n",
+            "  Cell State U_c[3, 0]: 0.024317285045981407\n",
+            "  Output Gate U_o[3, 0]: 0.020522702485322952\n",
+            "  Forget Gate b_f[3]: 0.0\n",
+            "  Input Gate b_i[3]: 1.0\n",
+            "  Cell State b_c[3]: 0.0\n",
+            "  Output Gate b_o[3]: 0.0\n",
+            "====\n",
+            "Feature: Adj Close\n",
+            "  Forget Gate W_f[4, 0]: 0.13921821117401123\n",
+            "  Input Gate W_i[4, 0]: 0.1498813033103943\n",
+            "  Cell State W_c[4, 0]: -0.11748667061328888\n",
+            "  Output Gate W_o[4, 0]: 0.05497296154499054\n",
+            "  Forget Gate U_f[4, 0]: -0.11227526515722275\n",
+            "  Input Gate U_i[4, 0]: 0.06800103187561035\n",
+            "  Cell State U_c[4, 0]: 0.0842202827334404\n",
+            "  Output Gate U_o[4, 0]: 0.06456313282251358\n",
+            "  Forget Gate b_f[4]: 0.0\n",
+            "  Input Gate b_i[4]: 1.0\n",
+            "  Cell State b_c[4]: 0.0\n",
+            "  Output Gate b_o[4]: 0.0\n",
+            "====\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ADRO"
+      ],
+      "metadata": {
+        "id": "LHohXfRvvOQi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_adro = create_lstm_model(100)\n",
+        "\n",
+        "start_time = time.time()\n",
+        "model_adro.fit(X_train_adro, y_train_adro, epochs=100, batch_size=32, validation_split=0.1)\n",
+        "\n",
+        "end_time = time.time()\n",
+        "elapsed_time = end_time - start_time\n",
+        "\n",
+        "print(f\"Waktu training: {elapsed_time:.2f} detik\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LOSVVOqEvLF7",
+        "outputId": "ba4313f8-c371-406d-ea24-137da451ef29"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 31ms/step - loss: 0.0634 - val_loss: 0.0047\n",
+            "Epoch 2/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0045 - val_loss: 0.0021\n",
+            "Epoch 3/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0025 - val_loss: 0.0016\n",
+            "Epoch 4/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0025 - val_loss: 8.3777e-04\n",
+            "Epoch 5/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0023 - val_loss: 0.0014\n",
+            "Epoch 6/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0026 - val_loss: 8.2497e-04\n",
+            "Epoch 7/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0024 - val_loss: 0.0016\n",
+            "Epoch 8/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0022 - val_loss: 0.0010\n",
+            "Epoch 9/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024 - val_loss: 0.0012\n",
+            "Epoch 10/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0020 - val_loss: 0.0011\n",
+            "Epoch 11/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 7.9596e-04\n",
+            "Epoch 12/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0028 - val_loss: 0.0013\n",
+            "Epoch 13/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0025 - val_loss: 0.0021\n",
+            "Epoch 14/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0023 - val_loss: 7.8227e-04\n",
+            "Epoch 15/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0023 - val_loss: 0.0011\n",
+            "Epoch 16/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0020 - val_loss: 0.0011\n",
+            "Epoch 17/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 0.0023 - val_loss: 0.0012\n",
+            "Epoch 18/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0018 - val_loss: 6.9405e-04\n",
+            "Epoch 19/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0021 - val_loss: 0.0010\n",
+            "Epoch 20/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0022 - val_loss: 0.0014\n",
+            "Epoch 21/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 0.0012\n",
+            "Epoch 22/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0020 - val_loss: 6.5237e-04\n",
+            "Epoch 23/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0020 - val_loss: 6.5597e-04\n",
+            "Epoch 24/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 6.5076e-04\n",
+            "Epoch 25/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0017 - val_loss: 0.0014\n",
+            "Epoch 26/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 6.7983e-04\n",
+            "Epoch 27/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0019 - val_loss: 6.8607e-04\n",
+            "Epoch 28/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0021 - val_loss: 6.1122e-04\n",
+            "Epoch 29/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 7.4124e-04\n",
+            "Epoch 30/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0018 - val_loss: 0.0011\n",
+            "Epoch 31/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0017 - val_loss: 7.7461e-04\n",
+            "Epoch 32/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0017 - val_loss: 5.9862e-04\n",
+            "Epoch 33/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0023 - val_loss: 6.0562e-04\n",
+            "Epoch 34/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 6.5603e-04\n",
+            "Epoch 35/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0019 - val_loss: 6.9457e-04\n",
+            "Epoch 36/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 5.3735e-04\n",
+            "Epoch 37/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0017 - val_loss: 0.0011\n",
+            "Epoch 38/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 0.0012\n",
+            "Epoch 39/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017 - val_loss: 0.0014\n",
+            "Epoch 40/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0018 - val_loss: 4.8382e-04\n",
+            "Epoch 41/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0014 - val_loss: 6.2043e-04\n",
+            "Epoch 42/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 0.0016 - val_loss: 6.2117e-04\n",
+            "Epoch 43/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 0.0011\n",
+            "Epoch 44/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 0.0012\n",
+            "Epoch 45/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 7.6431e-04\n",
+            "Epoch 46/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0022 - val_loss: 4.9380e-04\n",
+            "Epoch 47/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 7.1142e-04\n",
+            "Epoch 48/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0017 - val_loss: 4.5607e-04\n",
+            "Epoch 49/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0014 - val_loss: 0.0011\n",
+            "Epoch 50/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0013 - val_loss: 9.7056e-04\n",
+            "Epoch 51/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0015 - val_loss: 4.2991e-04\n",
+            "Epoch 52/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0012 - val_loss: 4.6422e-04\n",
+            "Epoch 53/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0012 - val_loss: 4.3565e-04\n",
+            "Epoch 54/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0015 - val_loss: 9.2766e-04\n",
+            "Epoch 55/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0017 - val_loss: 3.9524e-04\n",
+            "Epoch 56/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0012 - val_loss: 9.5586e-04\n",
+            "Epoch 57/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0013 - val_loss: 0.0011\n",
+            "Epoch 58/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0016 - val_loss: 3.7444e-04\n",
+            "Epoch 59/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0011 - val_loss: 6.2351e-04\n",
+            "Epoch 60/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0016 - val_loss: 5.4106e-04\n",
+            "Epoch 61/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0013 - val_loss: 3.5580e-04\n",
+            "Epoch 62/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0013 - val_loss: 3.3952e-04\n",
+            "Epoch 63/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0013 - val_loss: 5.4781e-04\n",
+            "Epoch 64/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 0.0010 - val_loss: 4.3097e-04\n",
+            "Epoch 65/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0011 - val_loss: 3.7820e-04\n",
+            "Epoch 66/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0012 - val_loss: 3.8035e-04\n",
+            "Epoch 67/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0012 - val_loss: 3.7441e-04\n",
+            "Epoch 68/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0012 - val_loss: 3.1452e-04\n",
+            "Epoch 69/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 8.8345e-04 - val_loss: 3.4853e-04\n",
+            "Epoch 70/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011 - val_loss: 3.1826e-04\n",
+            "Epoch 71/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0011 - val_loss: 3.0337e-04\n",
+            "Epoch 72/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0012 - val_loss: 3.3823e-04\n",
+            "Epoch 73/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0015 - val_loss: 3.3784e-04\n",
+            "Epoch 74/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0013 - val_loss: 6.0157e-04\n",
+            "Epoch 75/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0013 - val_loss: 7.3720e-04\n",
+            "Epoch 76/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 9.4736e-04 - val_loss: 3.1509e-04\n",
+            "Epoch 77/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0010 - val_loss: 5.0552e-04\n",
+            "Epoch 78/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0010 - val_loss: 2.9248e-04\n",
+            "Epoch 79/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011 - val_loss: 3.2384e-04\n",
+            "Epoch 80/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 9.8109e-04 - val_loss: 6.4628e-04\n",
+            "Epoch 81/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 9.6263e-04 - val_loss: 6.3153e-04\n",
+            "Epoch 82/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0011 - val_loss: 2.8827e-04\n",
+            "Epoch 83/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0010 - val_loss: 8.3648e-04\n",
+            "Epoch 84/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0014 - val_loss: 2.9226e-04\n",
+            "Epoch 85/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 9.8804e-04 - val_loss: 2.8313e-04\n",
+            "Epoch 86/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011 - val_loss: 4.0354e-04\n",
+            "Epoch 87/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0012 - val_loss: 6.6343e-04\n",
+            "Epoch 88/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0010 - val_loss: 2.8362e-04\n",
+            "Epoch 89/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 0.0011 - val_loss: 2.6641e-04\n",
+            "Epoch 90/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012 - val_loss: 3.0804e-04\n",
+            "Epoch 91/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0011 - val_loss: 2.9357e-04\n",
+            "Epoch 92/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 9.1025e-04 - val_loss: 4.8552e-04\n",
+            "Epoch 93/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0011 - val_loss: 3.1646e-04\n",
+            "Epoch 94/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 7.6501e-04 - val_loss: 8.0415e-04\n",
+            "Epoch 95/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 8.9690e-04 - val_loss: 3.7813e-04\n",
+            "Epoch 96/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0011 - val_loss: 2.6486e-04\n",
+            "Epoch 97/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 9.5229e-04 - val_loss: 3.0072e-04\n",
+            "Epoch 98/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 7.9825e-04 - val_loss: 2.3989e-04\n",
+            "Epoch 99/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 9.5839e-04 - val_loss: 0.0011\n",
+            "Epoch 100/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0014 - val_loss: 2.8011e-04\n",
+            "Waktu training: 57.64 detik\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_adro.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 302
+        },
+        "id": "GJBjljeAYYr_",
+        "outputId": "2bd5991f-9b21-4218-b699-2d3b2f639d86"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ lstm_8 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │        \u001b[38;5;34m42,400\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_9 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ lstm_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">42,400</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">80,400</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m368,705\u001b[0m (1.41 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">368,705</span> (1.41 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,901\u001b[0m (480.08 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,901</span> (480.08 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m245,804\u001b[0m (960.18 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">245,804</span> (960.18 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(model_adro.history.history['loss'], label='Training Loss')\n",
+        "plt.plot(model_adro.history.history['val_loss'], label='Validation Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "aI9DnLVzRaye",
+        "outputId": "ee04d252-04f8-4628-ac45-89ce25a069f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        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)"
+      ],
+      "metadata": {
+        "id": "68QaIN0wnG_d"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        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)"
+      ],
+      "metadata": {
+        "id": "PRfI-Ju4nIPk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Make predictions"
+      ],
+      "metadata": {
+        "id": "KlDnmsvS9yAt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_adro = model_adro.predict(X_test_adro)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GLRYn08EvYVC",
+        "outputId": "4adcdf7a-d4cc-4ed6-e1d8-d62d750a8c13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 63ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DSSA"
+      ],
+      "metadata": {
+        "id": "_oU4JdWGvTCD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_dssa = create_lstm_model(100)\n",
+        "\n",
+        "start_time = time.time()\n",
+        "model_dssa.fit(X_train_dssa, y_train_dssa, epochs=100, batch_size=32, validation_split=0.1)\n",
+        "\n",
+        "end_time = time.time()\n",
+        "elapsed_time = end_time - start_time\n",
+        "\n",
+        "print(f\"Waktu training: {elapsed_time:.2f} detik\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "96mJEkpbBTFX",
+        "outputId": "3a3722f5-0f28-46e6-abf4-f1d4da6b8a95"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 53ms/step - loss: 6.0729e-04 - val_loss: 1.1607e-04\n",
+            "Epoch 2/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 5.3662e-05 - val_loss: 4.0032e-05\n",
+            "Epoch 3/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 4.7505e-05 - val_loss: 4.6214e-05\n",
+            "Epoch 4/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 3.9637e-05 - val_loss: 2.3383e-05\n",
+            "Epoch 5/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 3.3811e-05 - val_loss: 3.4817e-05\n",
+            "Epoch 6/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 4.1101e-05 - val_loss: 2.0400e-05\n",
+            "Epoch 7/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 4.3098e-05 - val_loss: 2.6784e-05\n",
+            "Epoch 8/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 3.4498e-05 - val_loss: 2.7796e-05\n",
+            "Epoch 9/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 3.6598e-05 - val_loss: 1.9876e-05\n",
+            "Epoch 10/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 2.5330e-05 - val_loss: 2.1431e-05\n",
+            "Epoch 11/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.2087e-05 - val_loss: 2.9069e-05\n",
+            "Epoch 12/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 2.6639e-05 - val_loss: 1.9305e-05\n",
+            "Epoch 13/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 2.5186e-05 - val_loss: 3.1775e-05\n",
+            "Epoch 14/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 3.5259e-05 - val_loss: 1.7241e-05\n",
+            "Epoch 15/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 4.4257e-05 - val_loss: 3.6952e-05\n",
+            "Epoch 16/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 2.4323e-05 - val_loss: 2.3037e-05\n",
+            "Epoch 17/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 3.3564e-05 - val_loss: 1.7497e-05\n",
+            "Epoch 18/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 2.3239e-05 - val_loss: 1.6559e-05\n",
+            "Epoch 19/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 3.1591e-05 - val_loss: 1.6294e-05\n",
+            "Epoch 20/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 2.3948e-05 - val_loss: 1.5452e-05\n",
+            "Epoch 21/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 2.7339e-05 - val_loss: 1.7759e-05\n",
+            "Epoch 22/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 2.8005e-05 - val_loss: 1.9440e-05\n",
+            "Epoch 23/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 2.6300e-05 - val_loss: 1.7108e-05\n",
+            "Epoch 24/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 2.2018e-05 - val_loss: 1.3593e-05\n",
+            "Epoch 25/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.1934e-05 - val_loss: 1.3414e-05\n",
+            "Epoch 26/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 2.0099e-05 - val_loss: 1.6968e-05\n",
+            "Epoch 27/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.6595e-05 - val_loss: 1.3755e-05\n",
+            "Epoch 28/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 2.1161e-05 - val_loss: 2.2154e-05\n",
+            "Epoch 29/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 2.9517e-05 - val_loss: 2.4757e-05\n",
+            "Epoch 30/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 2.0809e-05 - val_loss: 1.3173e-05\n",
+            "Epoch 31/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 2.1917e-05 - val_loss: 1.7585e-05\n",
+            "Epoch 32/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 2.6970e-05 - val_loss: 1.3342e-05\n",
+            "Epoch 33/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - loss: 1.7368e-05 - val_loss: 1.6150e-05\n",
+            "Epoch 34/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 2.7698e-05 - val_loss: 1.9286e-05\n",
+            "Epoch 35/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 2.5868e-05 - val_loss: 1.5217e-05\n",
+            "Epoch 36/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 1.8167e-05 - val_loss: 2.5588e-05\n",
+            "Epoch 37/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 2.2198e-05 - val_loss: 1.6807e-05\n",
+            "Epoch 38/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.8543e-05 - val_loss: 1.0588e-05\n",
+            "Epoch 39/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.6675e-05 - val_loss: 1.1626e-05\n",
+            "Epoch 40/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.5276e-05 - val_loss: 1.8952e-05\n",
+            "Epoch 41/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 2.2376e-05 - val_loss: 1.7271e-05\n",
+            "Epoch 42/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 2.4067e-05 - val_loss: 1.3342e-05\n",
+            "Epoch 43/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.8935e-05 - val_loss: 1.1219e-05\n",
+            "Epoch 44/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.9269e-05 - val_loss: 4.3474e-05\n",
+            "Epoch 45/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 2.5148e-05 - val_loss: 1.0248e-05\n",
+            "Epoch 46/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.8836e-05 - val_loss: 8.6959e-06\n",
+            "Epoch 47/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.4780e-05 - val_loss: 8.6072e-06\n",
+            "Epoch 48/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.7145e-05 - val_loss: 8.5170e-06\n",
+            "Epoch 49/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.6996e-05 - val_loss: 8.4682e-06\n",
+            "Epoch 50/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.8741e-05 - val_loss: 8.2660e-06\n",
+            "Epoch 51/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.4392e-05 - val_loss: 7.8097e-06\n",
+            "Epoch 52/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.4907e-05 - val_loss: 1.4739e-05\n",
+            "Epoch 53/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.7821e-05 - val_loss: 8.8018e-06\n",
+            "Epoch 54/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.5245e-05 - val_loss: 1.1252e-05\n",
+            "Epoch 55/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.5844e-05 - val_loss: 7.5283e-06\n",
+            "Epoch 56/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.5407e-05 - val_loss: 8.9423e-06\n",
+            "Epoch 57/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.3309e-05 - val_loss: 1.9220e-05\n",
+            "Epoch 58/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.6204e-05 - val_loss: 7.4776e-06\n",
+            "Epoch 59/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.5898e-05 - val_loss: 7.0166e-06\n",
+            "Epoch 60/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.2958e-05 - val_loss: 1.7142e-05\n",
+            "Epoch 61/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.4782e-05 - val_loss: 6.7864e-06\n",
+            "Epoch 62/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.6536e-05 - val_loss: 7.0573e-06\n",
+            "Epoch 63/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.4730e-05 - val_loss: 7.5857e-06\n",
+            "Epoch 64/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.7158e-05 - val_loss: 1.6419e-05\n",
+            "Epoch 65/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.4458e-05 - val_loss: 7.7400e-06\n",
+            "Epoch 66/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.4316e-05 - val_loss: 6.2454e-06\n",
+            "Epoch 67/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.5386e-05 - val_loss: 7.0862e-06\n",
+            "Epoch 68/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 1.2294e-05 - val_loss: 1.1513e-05\n",
+            "Epoch 69/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.3214e-05 - val_loss: 6.1237e-06\n",
+            "Epoch 70/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 1.2710e-05 - val_loss: 6.4623e-06\n",
+            "Epoch 71/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.2616e-05 - val_loss: 7.6943e-06\n",
+            "Epoch 72/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.2407e-05 - val_loss: 7.1094e-06\n",
+            "Epoch 73/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.3236e-05 - val_loss: 6.1194e-06\n",
+            "Epoch 74/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.5359e-05 - val_loss: 8.1674e-06\n",
+            "Epoch 75/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.0886e-05 - val_loss: 7.8858e-06\n",
+            "Epoch 76/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.2566e-05 - val_loss: 8.4608e-06\n",
+            "Epoch 77/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.3648e-05 - val_loss: 1.0483e-05\n",
+            "Epoch 78/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.5020e-05 - val_loss: 6.9493e-06\n",
+            "Epoch 79/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 1.4242e-05 - val_loss: 5.6512e-06\n",
+            "Epoch 80/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.2484e-05 - val_loss: 1.1918e-05\n",
+            "Epoch 81/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.3433e-05 - val_loss: 1.0469e-05\n",
+            "Epoch 82/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.6092e-05 - val_loss: 8.0667e-06\n",
+            "Epoch 83/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.7249e-05 - val_loss: 5.3213e-06\n",
+            "Epoch 84/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 1.2485e-05 - val_loss: 1.1703e-05\n",
+            "Epoch 85/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.3541e-05 - val_loss: 1.8672e-05\n",
+            "Epoch 86/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.8206e-05 - val_loss: 6.9480e-06\n",
+            "Epoch 87/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.3479e-05 - val_loss: 1.5265e-05\n",
+            "Epoch 88/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.5031e-05 - val_loss: 5.2197e-06\n",
+            "Epoch 89/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.3136e-05 - val_loss: 4.8828e-06\n",
+            "Epoch 90/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.3792e-05 - val_loss: 5.2959e-06\n",
+            "Epoch 91/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.1710e-05 - val_loss: 5.3255e-06\n",
+            "Epoch 92/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 1.4487e-05 - val_loss: 1.2576e-05\n",
+            "Epoch 93/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 1.1391e-05 - val_loss: 6.8992e-06\n",
+            "Epoch 94/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 1.2469e-05 - val_loss: 5.2722e-06\n",
+            "Epoch 95/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.1185e-05 - val_loss: 4.9925e-06\n",
+            "Epoch 96/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - loss: 1.0215e-05 - val_loss: 5.2174e-06\n",
+            "Epoch 97/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.3569e-05 - val_loss: 4.8879e-06\n",
+            "Epoch 98/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 1.4445e-05 - val_loss: 5.8271e-06\n",
+            "Epoch 99/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 1.4308e-05 - val_loss: 6.0410e-06\n",
+            "Epoch 100/100\n",
+            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.7205e-05 - val_loss: 6.0648e-06\n",
+            "Waktu training: 81.70 detik\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_dssa.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 302
+        },
+        "id": "Dsn7vLiNYe0Z",
+        "outputId": "ecf174b3-f774-47af-eb03-7ed54cdd259d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │        \u001b[38;5;34m42,400\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m100\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ lstm_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">42,400</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">80,400</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m368,705\u001b[0m (1.41 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">368,705</span> (1.41 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,901\u001b[0m (480.08 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,901</span> (480.08 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m245,804\u001b[0m (960.18 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">245,804</span> (960.18 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(model_dssa.history.history['loss'], label='Training Loss')\n",
+        "plt.plot(model_dssa.history.history['val_loss'], label='Validation Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "xVpdraikRRxX",
+        "outputId": "9a00efd0-12ec-4669-f276-227e2d7fbaca"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Make predictions"
+      ],
+      "metadata": {
+        "id": "pJw3fvSKvWcK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_dssa = model_dssa.predict(X_test_dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RR2M3q-_vaZU",
+        "outputId": "1131bf3c-4428-4bf6-8ec2-ed93fe710b31"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Evaluate Model"
+      ],
+      "metadata": {
+        "id": "BNJ4gdxPF3f2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def evaluate_model(y_true, y_pred):\n",
+        "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
+        "    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
+        "    r2 = r2_score(y_true, y_pred)\n",
+        "\n",
+        "    print(f'RMSE: {rmse:.3f}')\n",
+        "    print(f'MAPE: {mape:.3f}%')\n",
+        "    print(f'R-squared: {r2:.5f}')\n",
+        "\n",
+        "    return rmse, mape, r2"
+      ],
+      "metadata": {
+        "id": "NFvlAVTVvypg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate ADRO model\n",
+        "print(\"Evaluation for ADRO:\")\n",
+        "rmse_adro, mape_adro, r2_adro = evaluate_model(y_test_adro, y_pred_adro)\n",
+        "\n",
+        "# Evaluate DSSA model\n",
+        "print(\"\\nEvaluation for DSSA:\")\n",
+        "rmse_dssa, mape_dssa, r2_dssa = evaluate_model(y_test_dssa, y_pred_dssa)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AE2uyUC5v0ZG",
+        "outputId": "6214b719-aa41-430a-b3c5-bf867434809c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluation for ADRO:\n",
+            "RMSE: 0.023\n",
+            "MAPE: 17.053%\n",
+            "R-squared: 0.95269\n",
+            "\n",
+            "Evaluation for DSSA:\n",
+            "RMSE: 0.039\n",
+            "MAPE: 115.017%\n",
+            "R-squared: 0.98061\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Invert"
+      ],
+      "metadata": {
+        "id": "oZQ0i9Hozuda"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### ADRO"
+      ],
+      "metadata": {
+        "id": "U1QWVjzn-jVZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dummy_input_adro = np.zeros((len(y_pred_adro), 5))\n",
+        "dummy_input_adro[:, 1] = y_pred_adro.reshape(-1)\n",
+        "dummy_input_adro"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-zc5rA_b4oOh",
+        "outputId": "45e92e5e-ed07-4146-9765-229b8aec8cbe"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.        , 0.59014332, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.58159274, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.57574308, 0.        , 0.        , 0.        ],\n",
+              "       ...,\n",
+              "       [0.        , 0.83483815, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.83351439, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.84000373, 0.        , 0.        , 0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inverted_adro = scaler.inverse_transform(dummy_input_adro)\n",
+        "pred_lstm_adro_real = inverted_adro[:, 1]\n",
+        "pred_lstm_adro_real = pred_lstm_adro_real / 10\n",
+        "pred_lstm_adro_real"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5LjQOTRK43O2",
+        "outputId": "74710e42-d281-4623-d3e9-7d4dfa5bf803"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2680.07354277, 2642.57495525, 2616.92127377, 2638.38320628,\n",
+              "       2654.42325944, 2690.1174292 , 2721.12554982, 2716.84989268,\n",
+              "       2641.32548156, 2570.72917244, 2545.21690628, 2524.19882447,\n",
+              "       2517.85735339, 2466.04549581, 2412.92796436, 2326.40661639,\n",
+              "       2262.77207145, 2275.22916731, 2328.40446731, 2325.6828104 ,\n",
+              "       2301.24540529, 2299.76616436, 2308.28428134, 2317.1040495 ,\n",
+              "       2323.23065293, 2350.15837935, 2362.01243424, 2374.73197186,\n",
+              "       2393.75298694, 2426.76209524, 2450.35806608, 2439.61180815,\n",
+              "       2416.07883379, 2412.01647592, 2428.26982835, 2440.12623581,\n",
+              "       2460.3546398 , 2458.20334932, 2444.28374189, 2406.72451046,\n",
+              "       2383.36405757, 2385.54906815, 2408.55193108, 2420.69718131,\n",
+              "       2415.10513306, 2355.35911754, 2327.67255804, 2344.74721724,\n",
+              "       2354.43717325, 2415.23400137, 2450.51176703, 2462.91762924,\n",
+              "       2463.88740903, 2464.49437094, 2455.53397164, 2336.17368427,\n",
+              "       2299.094899  , 2273.11342672, 2276.10040075, 2287.42225304,\n",
+              "       2289.78710419, 2280.491595  , 2286.81398416, 2290.19409803,\n",
+              "       2300.26020312, 2330.49197713, 2345.62629256, 2345.46004459,\n",
+              "       2326.17606497, 2298.7741659 , 2261.49619675, 2210.84493816,\n",
+              "       2175.4579089 , 2169.12401831, 2177.96103862, 2201.95799118,\n",
+              "       2211.24304453, 2229.46340314, 2244.90459767, 2250.39130327,\n",
+              "       2263.03085366, 2283.96764126, 2295.60813549, 2306.123842  ,\n",
+              "       2315.94475749, 2348.60176516, 2363.75908345, 2329.65184984,\n",
+              "       2291.43363866, 2264.24320909, 2255.62915972, 2255.25065807,\n",
+              "       2229.85131505, 2215.49491462, 2217.1545189 , 2230.81455994,\n",
+              "       2277.69988391, 2344.54568079, 2425.23580301, 2496.97127664,\n",
+              "       2552.33655375, 2569.92171967, 2530.20989078, 2519.36822325,\n",
+              "       2520.59626245, 2516.61598298, 2510.10669556, 2511.25448614,\n",
+              "       2522.64482424, 2529.54908127, 2539.42567414, 2584.13722757,\n",
+              "       2609.77574807, 2605.01624662, 2562.3989993 , 2547.34597808,\n",
+              "       2554.49777728, 2560.23150226, 2572.25023675, 2639.97746152,\n",
+              "       2667.85928395, 2652.16531932, 2647.95553401, 2653.68455386,\n",
+              "       2653.5112482 , 2614.83324116, 2552.28192195, 2505.59395209,\n",
+              "       2498.83163318, 2542.71299237, 2588.60396531, 2611.8826012 ,\n",
+              "       2674.90364933, 2713.90526482, 2746.67179748, 2807.42471105,\n",
+              "       2823.90756932, 2800.65010652, 2787.91959026, 2788.24581268,\n",
+              "       2802.49321091, 2810.19420347, 2817.88839972, 2811.59528694,\n",
+              "       2668.08513024, 2598.7597293 , 2596.9030323 , 2632.52061298,\n",
+              "       2732.65756464, 2800.50686142, 2806.48054808, 2784.87667745,\n",
+              "       2765.29287642, 2704.20589855, 2648.38709909, 2651.57665515,\n",
+              "       2670.17081028, 2676.68532562, 2621.4434275 , 2599.93182972,\n",
+              "       2611.62120503, 2609.72033209, 2598.35900897, 2612.13955364,\n",
+              "       2630.67489463, 2657.96543893, 2684.94073945, 2719.26257932,\n",
+              "       2768.10210106, 2787.90625906, 2805.59153971, 2815.16465163,\n",
+              "       2818.99279854, 2819.50801039, 2800.07869449, 2761.11184469,\n",
+              "       2757.74323225, 2773.59429598, 2796.30047426, 2862.61851227,\n",
+              "       2975.18955693, 3080.85033244, 3119.47684443, 3127.86452472,\n",
+              "       3115.63432074, 3121.15474644, 3137.74399295, 3168.36498585,\n",
+              "       3190.41976488, 3196.45226568, 3246.78096139, 3160.75025403,\n",
+              "       3126.98231265, 3164.26577112, 3161.91738793, 3152.904448  ,\n",
+              "       3172.80114025, 3192.11544183, 3191.05234361, 3177.43229619,\n",
+              "       3214.18564323, 3237.6903868 , 3243.10782242, 3242.27684399,\n",
+              "       3272.0786213 , 3374.78849545, 3451.02599531, 3505.37261155,\n",
+              "       3536.11776763, 3544.15517706, 3558.20574397, 3602.27374002,\n",
+              "       3615.01131397, 3585.83636469, 3557.11807451, 3513.36218548,\n",
+              "       3477.94352728, 3483.53191599, 3500.74589935, 3786.13771459,\n",
+              "       3787.30014336, 3765.5480611 , 3736.58039898, 3695.75293124,\n",
+              "       3704.37168574, 3725.714683  , 3753.1827153 , 3747.37736776,\n",
+              "       3775.83635294])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### DSSA"
+      ],
+      "metadata": {
+        "id": "HpdWzprW-pP8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dummy_input_dssa = np.zeros((len(y_pred_dssa), 5))\n",
+        "dummy_input_dssa[:, 1] = y_pred_dssa.reshape(-1)\n",
+        "dummy_input_dssa"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VnGXeC7y-qI5",
+        "outputId": "7350613f-d5f6-40ed-832d-5f2ced802022"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.        , 0.09492499, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.09493433, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.09503628, 0.        , 0.        , 0.        ],\n",
+              "       ...,\n",
+              "       [0.        , 0.86946911, 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.8616249 , 0.        , 0.        , 0.        ],\n",
+              "       [0.        , 0.87051117, 0.        , 0.        , 0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inverted_dssa = scaler.inverse_transform(dummy_input_dssa)\n",
+        "pred_lstm_dssa_real = inverted_dssa[:, 1]\n",
+        "pred_lstm_dssa_real"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rUEqSHno-yof",
+        "outputId": "cb62e813-46f5-4d5c-b2ee-fc5a0a61acbe"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 5082.93527693,  5083.34501542,  5087.81587016,  5089.03168909,\n",
+              "        5070.51046349,  5085.22804808,  5060.38038168,  4971.20083071,\n",
+              "        4949.66178633,  4949.40039016,  4956.51494041,  4960.5077669 ,\n",
+              "        4998.44451975,  5007.93875538,  5042.28523184,  5056.97112214,\n",
+              "        5057.96344735,  5056.33690968,  5055.07828712,  4972.53362443,\n",
+              "        4951.61768317,  5033.10499836,  5056.30488865,  5058.93649459,\n",
+              "        5063.1773207 ,  5057.11587027,  5054.88027953,  5095.43720223,\n",
+              "        5105.8770385 ,  5106.34199694,  5105.13794083,  5104.13515978,\n",
+              "        5103.68163742,  5103.53460208,  5103.53460208,  5103.53460208,\n",
+              "        5103.53460208,  5103.53460208,  5103.53460208,  5103.53460208,\n",
+              "        5103.53460208,  5103.53460208,  5103.53460208,  5619.75021377,\n",
+              "        6542.94397786,  7578.27408478,  8144.0739359 ,  8115.44190645,\n",
+              "        7991.30290501,  7802.81862244,  7816.77587092,  7867.07633585,\n",
+              "        7879.92657155,  7885.57664976,  7885.12966231,  7884.10172187,\n",
+              "        7635.66688128,  7576.83509886,  7575.19026347,  7582.8720434 ,\n",
+              "        7596.63977966,  7601.30047336,  7602.21274599,  7601.96049869,\n",
+              "        8482.51531959,  9454.35687132, 10665.18117815, 11893.46348166,\n",
+              "       12961.06686667, 13863.40775132, 14002.88613349, 13597.00584501,\n",
+              "       13203.64381909, 14189.75825518, 14803.03287864, 14536.59960479,\n",
+              "       14101.38543814, 14488.40076506, 14122.75718898, 13997.6033169 ,\n",
+              "       12459.00716454, 11855.22187553, 11645.57365209, 11701.30919687,\n",
+              "       11763.18036325, 11792.19141714, 12486.90859169, 12624.88133192,\n",
+              "       11368.510703  , 11559.61025424, 11724.57018852, 11765.05195983,\n",
+              "       12489.62057695, 12619.40116122, 12654.4661504 , 12641.62114263,\n",
+              "       12625.41196615, 12615.03976613, 12609.33348775, 11872.22177543,\n",
+              "       11703.34285907, 11701.44120194, 10928.72275688, 11536.0283988 ,\n",
+              "       11849.59597647, 11911.23450026, 12212.38510638, 12268.51993382,\n",
+              "       12259.90431607, 12239.94671851, 11207.21488953, 11747.55671419,\n",
+              "       12013.58547747, 12065.46085432, 12062.00650394, 12051.62123412,\n",
+              "       12044.46420699, 11363.88791174, 11782.23418355, 12022.32264444,\n",
+              "       12063.54612738, 12056.26493707, 12045.09678572, 12038.44686717,\n",
+              "       12037.27319837, 12041.49474651, 12207.3205556 , 12244.46495131,\n",
+              "       12244.68060315, 12239.59121972, 12070.86522013, 12076.96882069,\n",
+              "       12204.40076038, 12238.00062403, 12489.37878549, 12540.05435392,\n",
+              "       12540.69869548, 12420.58062062, 12506.38522029, 12527.08779693,\n",
+              "       12721.3522023 , 14709.89742339, 17105.01824677, 17566.0975644 ,\n",
+              "       19206.80551648, 20561.15657866, 20708.74347001, 20610.74343204,\n",
+              "       20474.46715295, 19550.68263352, 18780.96763045, 18900.28191209,\n",
+              "       18987.14385927, 18661.49389714, 19329.21211481, 19905.00252753,\n",
+              "       21741.11718625, 21199.58535552, 22520.43588459, 23199.92215782,\n",
+              "       23244.61828887, 23135.50629973, 23078.50363702, 23179.65088487,\n",
+              "       23353.33818376, 23921.92190409, 25692.41566837, 26455.85454941,\n",
+              "       26000.05804837, 25920.8863765 , 25899.90671992, 26250.94086409,\n",
+              "       27035.84821254, 27136.3262862 , 26874.74452525, 27291.09895825,\n",
+              "       27761.39249086, 27810.14549047, 27793.56774539, 28126.50281876,\n",
+              "       27490.33250481, 27556.05534375, 27721.51650518, 27797.82850295,\n",
+              "       27779.43405449, 27995.56947738, 28546.27631366, 28460.93307823,\n",
+              "       28372.36421406, 28419.58020419, 29820.56172907, 30115.515939  ,\n",
+              "       30438.19382668, 30577.47877568, 30338.51039737, 30242.90997624,\n",
+              "       34224.21673864, 39007.46603787, 39707.59215266, 38833.31606776,\n",
+              "       38258.69409591, 38388.92689556, 38325.82324624, 38655.07001966,\n",
+              "       38845.08673728, 38807.05620855, 38992.71283805, 39328.36643159,\n",
+              "       39157.92828709, 39343.62673998, 39968.23027343, 39174.32044089,\n",
+              "       38936.38196349, 38811.69076264, 38815.80513835, 38915.20625979,\n",
+              "       38927.27230698, 39240.06680548, 39046.83230102, 39116.85510695,\n",
+              "       39078.88208538, 38845.49712926, 38372.45893687, 37957.48206168,\n",
+              "       38529.78022158, 39018.76619428, 39050.56765229, 38706.55983716,\n",
+              "       39096.26754463])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Simpan Prediksi"
+      ],
+      "metadata": {
+        "id": "oW1oPxfEADez"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "adro_actual = adro['High'].iloc[919:]\n",
+        "dssa_actual = dssa['High'].iloc[919:]"
+      ],
+      "metadata": {
+        "id": "68DFmVT-AugO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create dataframes for predictions\n",
+        "df_pred_adro = pd.DataFrame({'Actual ADRO': adro_actual.values, 'Predicted ADRO': pred_lstm_adro_real.flatten()})\n",
+        "df_pred_dssa = pd.DataFrame({'Actual DSSA': dssa_actual.values, 'Predicted DSSA': pred_lstm_dssa_real.flatten()})"
+      ],
+      "metadata": {
+        "id": "zHtrPQLpBrR9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save predictions to CSV files\n",
+        "df_pred_adro.to_csv('LSTM_adro_predictions.csv', index=False)\n",
+        "df_pred_dssa.to_csv('LSTM_dssa_predictions.csv', index=False)\n",
+        "\n",
+        "print(\"ADRO predictions saved to 'LSTM_adro_predictions.csv'\")\n",
+        "print(\"DSSA predictions saved to 'LSTM_dssa_predictions.csv'\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vIZ6f_P6nWRH",
+        "outputId": "ef1b5df3-4aa6-4188-bcc2-2e8970a3a700"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ADRO predictions saved to 'LSTM_adro_predictions.csv'\n",
+            "DSSA predictions saved to 'LSTM_dssa_predictions.csv'\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: downloadkan csv adro dan dssa tersebut\n",
+        "\n",
+        "from google.colab import files\n",
+        "\n",
+        "files.download('LSTM_adro_predictions.csv')\n",
+        "files.download('LSTM_dssa_predictions.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "gF4qL7w4n5zD",
+        "outputId": "dab98912-503e-4e26-b3eb-1b527447c24d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_cbbef4c8-92a3-46c9-836d-819f02e6c57e\", \"LSTM_adro_predictions.csv\", 5857)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_0caeb2af-ce0e-4324-8ea0-f3e5ba20d6de\", \"LSTM_dssa_predictions.csv\", 6008)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "AFFm5NWUHa1G"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/PT Adaro Energy Indonesia Tbk_2020-2024.csv b/PT Adaro Energy Indonesia Tbk_2020-2024.csv
new file mode 100644
index 0000000..6a33632
--- /dev/null
+++ b/PT Adaro Energy Indonesia Tbk_2020-2024.csv	
@@ -0,0 +1,1149 @@
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diff --git a/PT Dian Swastatika Sentosa Tbk_2020-2024.csv b/PT Dian Swastatika Sentosa Tbk_2020-2024.csv
new file mode 100644
index 0000000..c6961f1
--- /dev/null
+++ b/PT Dian Swastatika Sentosa Tbk_2020-2024.csv	
@@ -0,0 +1,1149 @@
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