From fe7bb529eda1f00e322216cc99f297b1d78e1ece Mon Sep 17 00:00:00 2001
From: 2110511019 <2110511019@mahasiswa.upnvj.ac.id>
Date: Fri, 11 Jul 2025 06:25:57 +0000
Subject: [PATCH] Code Skripsi

This is full code for prediction of fuel adulteration
---
 Combine.py                           |   22 +
 Concat.py                            |   62 +
 Fuel_All_External.xlsx               |  Bin 0 -> 8922 bytes
 Fuel_All_new.csv                     |  121 +
 Fuel_All_new.xlsx                    |  Bin 0 -> 13271 bytes
 Fuel_Prediction_New_(1).ipynb        | 3444 ++++++++++++++++++++++++++
 Uji_model_skripsi (1).ipynb          |  418 ++++
 detect2.py                           |   96 +
 model_new.h5                         |  Bin 0 -> 273776 bytes
 model_new.tflite                     |  Bin 0 -> 80556 bytes
 model_tuning_spektroskopi_new.h5     |  Bin 0 -> 193496 bytes
 model_tuning_spektroskopi_new.tflite |  Bin 0 -> 53732 bytes
 scaler_X.pkl                         |  Bin 0 -> 791 bytes
 scaler_y.pkl                         |  Bin 0 -> 511 bytes
 scan_with_button.py                  |   63 +
 15 files changed, 4226 insertions(+)
 create mode 100644 Combine.py
 create mode 100644 Concat.py
 create mode 100644 Fuel_All_External.xlsx
 create mode 100644 Fuel_All_new.csv
 create mode 100644 Fuel_All_new.xlsx
 create mode 100644 Fuel_Prediction_New_(1).ipynb
 create mode 100644 Uji_model_skripsi (1).ipynb
 create mode 100644 detect2.py
 create mode 100644 model_new.h5
 create mode 100644 model_new.tflite
 create mode 100644 model_tuning_spektroskopi_new.h5
 create mode 100644 model_tuning_spektroskopi_new.tflite
 create mode 100644 scaler_X.pkl
 create mode 100644 scaler_y.pkl
 create mode 100644 scan_with_button.py

diff --git a/Combine.py b/Combine.py
new file mode 100644
index 0000000..36fe2b7
--- /dev/null
+++ b/Combine.py
@@ -0,0 +1,22 @@
+import pandas as pd
+
+labels = [100, 90, 80, 70, 60, 50]
+
+df = pd.DataFrame()
+for label in labels:
+    df_temp = pd.read_csv(f"Fuel_{label}_new.csv")
+    df = pd.concat([df, df_temp], axis=0)
+
+df.to_csv("Fuel_All.csv", index=False)
+
+
+# import pandas as pd
+
+# labels = [100,90,80,70,60,50]
+
+# df = pd.DataFrame()
+# for label in labels:
+#     df_temp = pd.read_excel(f"Fuel_{label}_new.xlsx")
+#     df = pd.concat([df, df_temp], axis=0)
+
+# df.to_excel("Fuel_All_External.xlsx", index=False)
diff --git a/Concat.py b/Concat.py
new file mode 100644
index 0000000..161c9b9
--- /dev/null
+++ b/Concat.py
@@ -0,0 +1,62 @@
+import os
+import pandas as pd
+
+# Path folder yang berisi data txt
+label = 100
+path = "pertalite_murni/"
+# path = f"{label}/"
+
+# Simpan Data
+myData = []
+for dir, folder, files in os.walk(path):
+    # Menggabungkan setiap data dalam folder tertentu
+    for file in files:
+        df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
+        data_values = list(df_temp.columns)
+        myData.append(data_values)
+
+# 18 Kolom
+columns = ['410nm', '435nm', '460nm','485nm',
+           '510nm', '535nm', '560nm', '585nm',
+           '610nm', '645nm', '680nm', '705nm',
+           '730nm', '760nm', '810nm', '860nm',
+           '900nm', '940nm']
+
+# Simpan ke dalam CSV
+df = pd.DataFrame(myData, columns=columns)
+df['Label'] = label
+df.to_excel(f"Fuel_Ron90{label}_new2.xlsx", index=False)
+
+
+# import os
+# import pandas as pd
+
+# # Path folder yang berisi data txt
+# label = 100
+# path = f"{label}/"
+
+# # Simpan Data
+# myData = []
+# for dir, folder, files in os.walk(path):
+#     # Menggabungkan setiap data dalam folder tertentu
+#     for file in files:
+#         df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
+#         data_values = list(df_temp.columns)
+#         myData.append(data_values)
+
+# # 18 Kolom
+# columns = ['410nm', '435nm', '460nm','485nm',
+#            '510nm', '535nm', '560nm', '585nm',
+#            '610nm', '645nm', '680nm', '705nm',
+#            '730nm', '760nm', '810nm', '860nm',
+#            '900nm', '940nm']
+
+# # Simpan ke dalam Excel
+# df = pd.DataFrame(myData, columns=columns)
+# df['Label'] = label
+# df.head()
+# df.to_excel(f"Fuel_{label}_new.xlsx", index=False)
+
+
+
+
diff --git a/Fuel_All_External.xlsx b/Fuel_All_External.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..de7fbdafd42965bd37940df6c4a36807cfc6665b
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diff --git a/Fuel_All_new.csv b/Fuel_All_new.csv
new file mode 100644
index 0000000..26b6b8c
--- /dev/null
+++ b/Fuel_All_new.csv
@@ -0,0 +1,121 @@
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diff --git a/Fuel_Prediction_New_(1).ipynb b/Fuel_Prediction_New_(1).ipynb
new file mode 100644
index 0000000..9777959
--- /dev/null
+++ b/Fuel_Prediction_New_(1).ipynb
@@ -0,0 +1,3444 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aSM7te3obX_r"
+      },
+      "source": [
+        "# Import Library yang Digunakan"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tvPLPO37bC0P"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.layers import Conv1D, Dense, Flatten, MaxPooling1D\n",
+        "from tensorflow.keras.optimizers import Adam,RMSprop\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.losses import MeanSquaredError\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.metrics import r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5TATamBXmoln"
+      },
+      "source": [
+        "# Preparing Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "ef70CMG2bqkm",
+        "outputId": "8fbe8d49-bf79-45ed-a4be-ec336445ac93"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 120 entries, 0 to 119\n",
+            "Data columns (total 19 columns):\n",
+            " #   Column  Non-Null Count  Dtype  \n",
+            "---  ------  --------------  -----  \n",
+            " 0   410nm   120 non-null    float64\n",
+            " 1   435nm   120 non-null    float64\n",
+            " 2   460nm   120 non-null    float64\n",
+            " 3   485nm   120 non-null    float64\n",
+            " 4   510nm   120 non-null    float64\n",
+            " 5   535nm   120 non-null    float64\n",
+            " 6   560nm   120 non-null    float64\n",
+            " 7   585nm   120 non-null    float64\n",
+            " 8   610nm   120 non-null    float64\n",
+            " 9   645nm   120 non-null    float64\n",
+            " 10  680nm   120 non-null    float64\n",
+            " 11  705nm   120 non-null    float64\n",
+            " 12  730nm   120 non-null    float64\n",
+            " 13  760nm   120 non-null    float64\n",
+            " 14  810nm   120 non-null    float64\n",
+            " 15  860nm   120 non-null    float64\n",
+            " 16  900nm   120 non-null    float64\n",
+            " 17  940nm   120 non-null    float64\n",
+            " 18  Label   120 non-null    int64  \n",
+            "dtypes: float64(18), int64(1)\n",
+            "memory usage: 17.9 KB\n",
+            "None\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 120,\n  \"fields\": [\n    {\n      \"column\": \"410nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.547008593038939,\n        \"min\": 0.82595294713974,\n        \"max\": 6.60762357711792,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.95571756362915,\n          2.477858781814575,\n          5.781670570373535\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"435nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3555981546971425,\n        \"min\": 0.9738456606864928,\n        \"max\": 2.921536922454834,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9738456606864928,\n          1.947691321372986,\n          2.921536922454834\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"460nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0839321206050467,\n        \"min\": 8.808282852172852,\n        \"max\": 12.7230749130249,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.786980628967283,\n          12.7230749130249,\n          10.76567840576172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"485nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36270200079114706,\n        \"min\": 0.9327982664108276,\n        \"max\": 2.798394918441772,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.865596532821655,\n          0.9327982664108276,\n          2.798394918441772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"510nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.534502929593268,\n        \"min\": 1.538316011428833,\n        \"max\": 4.614948272705078,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.845790147781372,\n          1.538316011428833,\n          4.614948272705078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"535nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3480740348416023,\n        \"min\": 23.16461753845215,\n        \"max\": 29.1425838470459,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          27.64809226989746,\n          26.90084648132324,\n          28.39533805847168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"560nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.5287637114524841,\n        \"max\": 0.5287637114524841,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.5287637114524841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"585nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.4831080138683319,\n        \"max\": 0.4831080138683319,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4831080138683319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"610nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43682813741550336,\n        \"min\": 1.14484703540802,\n        \"max\": 2.28969407081604,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.28969407081604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"645nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.4147160351276397,\n        \"max\": 0.4147160351276397,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4147160351276397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"680nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3971450397971249,\n        \"min\": 1.02208936214447,\n        \"max\": 2.04417872428894,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.04417872428894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"705nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.3965269923210144,\n        \"max\": 0.3965269923210144,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.3965269923210144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"730nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5046979408918056,\n        \"min\": 0.7796502709388733,\n        \"max\": 2.338950872421265,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.7796502709388733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"760nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.8432264924049377,\n        \"max\": 0.8432264924049377,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8432264924049377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"810nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41654401584787726,\n        \"min\": 0.8245857357978821,\n        \"max\": 2.473757266998291,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.649171471595764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"860nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2087775400024961,\n        \"min\": 1.158214211463928,\n        \"max\": 2.316428422927856,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.316428422927856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"900nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.6376231908798218,\n        \"max\": 0.6376231908798218,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.6376231908798218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"940nm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.939268171787262,\n        \"max\": 0.939268171787262,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.939268171787262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 50,\n        \"max\": 100,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6e7eb4a5-c243-49fc-a928-79ae89b71b5d\" 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>410nm</th>\n",
+              "      <th>435nm</th>\n",
+              "      <th>460nm</th>\n",
+              "      <th>485nm</th>\n",
+              "      <th>510nm</th>\n",
+              "      <th>535nm</th>\n",
+              "      <th>560nm</th>\n",
+              "      <th>585nm</th>\n",
+              "      <th>610nm</th>\n",
+              "      <th>645nm</th>\n",
+              "      <th>680nm</th>\n",
+              "      <th>705nm</th>\n",
+              "      <th>730nm</th>\n",
+              "      <th>760nm</th>\n",
+              "      <th>810nm</th>\n",
+              "      <th>860nm</th>\n",
+              "      <th>900nm</th>\n",
+              "      <th>940nm</th>\n",
+              "      <th>Label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.781671</td>\n",
+              "      <td>0.973846</td>\n",
+              "      <td>8.808283</td>\n",
+              "      <td>1.865597</td>\n",
+              "      <td>2.307474</td>\n",
+              "      <td>26.153601</td>\n",
+              "      <td>0.528764</td>\n",
+              "      <td>0.483108</td>\n",
+              "      <td>1.144847</td>\n",
+              "      <td>0.414716</td>\n",
+              "      <td>1.022089</td>\n",
+              "      <td>0.396527</td>\n",
+              "      <td>0.77965</td>\n",
+              "      <td>0.843226</td>\n",
+              "      <td>1.649171</td>\n",
+              "      <td>1.158214</td>\n",
+              "      <td>0.637623</td>\n",
+              "      <td>0.939268</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5.781671</td>\n",
+              "      <td>0.973846</td>\n",
+              "      <td>8.808283</td>\n",
+              "      <td>1.865597</td>\n",
+              "      <td>2.307474</td>\n",
+              "      <td>26.153601</td>\n",
+              "      <td>0.528764</td>\n",
+              "      <td>0.483108</td>\n",
+              "      <td>1.144847</td>\n",
+              "      <td>0.414716</td>\n",
+              "      <td>1.022089</td>\n",
+              "      <td>0.396527</td>\n",
+              "      <td>0.77965</td>\n",
+              "      <td>0.843226</td>\n",
+              "      <td>1.649171</td>\n",
+              "      <td>1.158214</td>\n",
+              "      <td>0.637623</td>\n",
+              "      <td>0.939268</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>5.781671</td>\n",
+              "      <td>0.973846</td>\n",
+              "      <td>8.808283</td>\n",
+              "      <td>1.865597</td>\n",
+              "      <td>2.307474</td>\n",
+              "      <td>26.153601</td>\n",
+              "      <td>0.528764</td>\n",
+              "      <td>0.483108</td>\n",
+              "      <td>1.144847</td>\n",
+              "      <td>0.414716</td>\n",
+              "      <td>1.022089</td>\n",
+              "      <td>0.396527</td>\n",
+              "      <td>0.77965</td>\n",
+              "      <td>0.843226</td>\n",
+              "      <td>1.649171</td>\n",
+              "      <td>1.158214</td>\n",
+              "      <td>0.637623</td>\n",
+              "      <td>0.939268</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5.781671</td>\n",
+              "      <td>0.973846</td>\n",
+              "      <td>8.808283</td>\n",
+              "      <td>1.865597</td>\n",
+              "      <td>2.307474</td>\n",
+              "      <td>26.153601</td>\n",
+              "      <td>0.528764</td>\n",
+              "      <td>0.483108</td>\n",
+              "      <td>1.144847</td>\n",
+              "      <td>0.414716</td>\n",
+              "      <td>1.022089</td>\n",
+              "      <td>0.396527</td>\n",
+              "      <td>0.77965</td>\n",
+              "      <td>0.843226</td>\n",
+              "      <td>1.649171</td>\n",
+              "      <td>1.158214</td>\n",
+              "      <td>0.637623</td>\n",
+              "      <td>0.939268</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.781671</td>\n",
+              "      <td>0.973846</td>\n",
+              "      <td>8.808283</td>\n",
+              "      <td>1.865597</td>\n",
+              "      <td>2.307474</td>\n",
+              "      <td>26.153601</td>\n",
+              "      <td>0.528764</td>\n",
+              "      <td>0.483108</td>\n",
+              "      <td>1.144847</td>\n",
+              "      <td>0.414716</td>\n",
+              "      <td>1.022089</td>\n",
+              "      <td>0.396527</td>\n",
+              "      <td>0.77965</td>\n",
+              "      <td>0.843226</td>\n",
+              "      <td>1.649171</td>\n",
+              "      <td>1.158214</td>\n",
+              "      <td>0.637623</td>\n",
+              "      <td>0.939268</td>\n",
+              "      <td>100</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-6e7eb4a5-c243-49fc-a928-79ae89b71b5d')\"\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-6e7eb4a5-c243-49fc-a928-79ae89b71b5d 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-6e7eb4a5-c243-49fc-a928-79ae89b71b5d');\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-ee73326d-ec6c-4619-859c-0f610db5f97d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee73326d-ec6c-4619-859c-0f610db5f97d')\"\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-ee73326d-ec6c-4619-859c-0f610db5f97d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "      410nm     435nm     460nm     485nm     510nm      535nm     560nm  \\\n",
+              "0  5.781671  0.973846  8.808283  1.865597  2.307474  26.153601  0.528764   \n",
+              "1  5.781671  0.973846  8.808283  1.865597  2.307474  26.153601  0.528764   \n",
+              "2  5.781671  0.973846  8.808283  1.865597  2.307474  26.153601  0.528764   \n",
+              "3  5.781671  0.973846  8.808283  1.865597  2.307474  26.153601  0.528764   \n",
+              "4  5.781671  0.973846  8.808283  1.865597  2.307474  26.153601  0.528764   \n",
+              "\n",
+              "      585nm     610nm     645nm     680nm     705nm    730nm     760nm  \\\n",
+              "0  0.483108  1.144847  0.414716  1.022089  0.396527  0.77965  0.843226   \n",
+              "1  0.483108  1.144847  0.414716  1.022089  0.396527  0.77965  0.843226   \n",
+              "2  0.483108  1.144847  0.414716  1.022089  0.396527  0.77965  0.843226   \n",
+              "3  0.483108  1.144847  0.414716  1.022089  0.396527  0.77965  0.843226   \n",
+              "4  0.483108  1.144847  0.414716  1.022089  0.396527  0.77965  0.843226   \n",
+              "\n",
+              "      810nm     860nm     900nm     940nm  Label  \n",
+              "0  1.649171  1.158214  0.637623  0.939268    100  \n",
+              "1  1.649171  1.158214  0.637623  0.939268    100  \n",
+              "2  1.649171  1.158214  0.637623  0.939268    100  \n",
+              "3  1.649171  1.158214  0.637623  0.939268    100  \n",
+              "4  1.649171  1.158214  0.637623  0.939268    100  "
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.read_csv(\"Fuel_All_new.csv\")\n",
+        "print(df.info())\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_p6NN6w8jOkZ",
+        "outputId": "c94234b6-00d6-4650-bda7-5b99afbae2a6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['410nm', '435nm', '460nm', '485nm', '510nm', '535nm', '560nm', '585nm',\n",
+            "       '610nm', '645nm', '680nm', '705nm', '730nm', '760nm', '810nm', '860nm',\n",
+            "       '900nm', '940nm'],\n",
+            "      dtype='object')\n",
+            "Index([410.0, 435.0, 460.0, 485.0, 510.0, 535.0, 560.0, 585.0, 610.0, 645.0,\n",
+            "       680.0, 705.0, 730.0, 760.0, 810.0, 860.0, 900.0, 940.0],\n",
+            "      dtype='float64')\n"
+          ]
+        }
+      ],
+      "source": [
+        "df_100 = df[df['Label'] == 100]\n",
+        "df_90 = df[df['Label'] == 90]\n",
+        "df_80 = df[df['Label'] == 80]\n",
+        "df_70 = df[df['Label'] == 70]\n",
+        "df_60 = df[df['Label'] == 60]\n",
+        "df_50 = df[df['Label'] == 50]\n",
+        "\n",
+        "# Ambil rata-rata untuk setiap panjang gelombang\n",
+        "wavelengths = df_100.columns[:-1]\n",
+        "print(wavelengths)\n",
+        "\n",
+        "mean_spectrum = df_100[wavelengths].mean()\n",
+        "mean_spectrum_90 = df_90[wavelengths].mean()\n",
+        "mean_spectrum_80 = df_80[wavelengths].mean()\n",
+        "mean_spectrum_70 = df_70[wavelengths].mean()\n",
+        "mean_spectrum_60 = df_60[wavelengths].mean()\n",
+        "mean_spectrum_50 = df_50[wavelengths].mean()\n",
+        "\n",
+        "wavelengths_numeric = mean_spectrum.index.str.replace('nm', '').astype(float)\n",
+        "print(wavelengths_numeric)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "pgC-1lppIIjc"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib\n",
+        "\n",
+        "plt.rcParams['font.family'] = 'DejaVu Serif'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "62m_2GsxJd5Y"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 100% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "nvS8oCuWIHdq",
+        "outputId": "5e49b1f2-8f21-43f2-b61e-b80ad24c5b39"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_100.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='blue')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 100% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ntt8Fm0oJlsA"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 90% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "kbSV58G3JyjH",
+        "outputId": "7c00a3f3-7897-456c-e64e-da7d55bd6d99"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_90.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='orange')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 90% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ni4PUz_RJpXA"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 80% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "JcCSUkrKKPfi",
+        "outputId": "1c6825aa-8999-40ef-88d9-291794622c6d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_80.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='green')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 80% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XGhTGZrsJq54"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 70% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "LZbhuWI5KS71",
+        "outputId": "66598b97-087f-424a-c5f7-76e4618e7d70"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_70.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='red')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 70% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jt2z7M0VJsjE"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 60% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "5YbMm4-cKYLI",
+        "outputId": "cfc31107-c464-4585-8234-5a719923f85a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_60.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='purple')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 60% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Vm3hPBGnJuQq"
+      },
+      "source": [
+        "# Data Spektroskopi Sampel 50% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "hABSaoHcKb0Z",
+        "outputId": "4c91a442-1026-480f-dace-478e2c3bd384"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for index, row in df_50.iterrows():\n",
+        "    plt.plot(wavelengths, row[:-1].values, linestyle='-', alpha=0.6, color='brown')\n",
+        "\n",
+        "plt.title(\"Data Spektroskopi Sampel 50% RON 92\")\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Spectral Value\")\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eM5dv9uLEkof"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 100% RON 92\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "P8GWkQETEj4X",
+        "outputId": "4c4218d2-78d2-406f-e499-9be23811301b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum.values, linestyle='-', label=\"100% RON 92\", color='blue')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 100% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SKecpFA-E_YN"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 90% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "OtvDSMdGFDS-",
+        "outputId": "94448c23-b3b4-4c3c-e318-24a7fb78119d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_90.values, linestyle='-', label=\"90% RON 92\", color='orange')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 90% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "upxFtTh9FXDa"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 80% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "s_lOg8F4FiO_",
+        "outputId": "8833fb47-da5f-42b9-cc7a-f6a802b4aacc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_80.values, linestyle='-', label=\"80% RON 92\", color='green')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 80% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RqfZzV18Fb_V"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 70% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "lYdR9qzIFpdj",
+        "outputId": "64ba81a7-b19b-4e6b-c502-acae628fa872"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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ee+01HDhwAD179kRYWBhGjRqV4rK2bt2K9u3bY+HChVZ9uQClGRQA1K5d22p63bp1AQC//fYb+vbta/VcSttz1apVOHbsWLJt+Cw5cuTAokWL8OGHH2Lx4sVYt24dVqxYgWXLliEkJARr165F0aJFASif8W+//RY//PAD7ty5A29vbzx8+BCAsu8UKFDA6r3Lli1r9Thv3rzw8/ODt/fjr/98+fKl2tTTHvtNen355Zfo3LkzAgMDM/wehQoVStYk9uLFi5g4cSJ27NiBHTt24O2338aBAwcAANWrV0epUqXSHHtaj3e2kC9fPqxYsQINGjRAt27dsHv37jR//rJly4YPPvgAY8aMwddff40BAwbg0aNHGDx4MLJnz46HDx/Cz88vxdfeu3cPAwYMwMSJExEREWHLVSLSJfZJInIhfn5+mDVrFtauXZvsZFBE0KFDByxduhSLFy/GsWPHcPjwYQwcOFA7AX+WjRs3IiIiAnXr1sXhw4e1G6AMW6t6cmjeJ4etTSpXrlyYPXs2Zs+ejSlTplg99+jRI4SHh+PgwYNYt24djh49qg3bnHR5T/PNN9+gR48e6Nu3r7bO69atSxZzRl26dAkAtGvt6CVmo9GIiIgIzJ07F5cuXcLOnTtRtWpVTJs2DZs3b042v7+/v9XjvHnzAgCuXr0KANqwyq1bt7b6386ZMwcFCxZMlmivXbsW27dvh5eXV4ZPBnPmzJlsmhqHGl9S+fLlg9lsxt27d2EwGLB9+3aMGDECK1asQN26dVGsWDFMnz492fDEt27dwptvvomgoCCMHj062RDPqS0zX7582uuf9KztmRElS5bE+++/jz179uDKlSsYNGgQjhw5gtGjR2vzjBs3DkOGDMEHH3yA48eP4/Dhw/jmm28ApLzvPNm3x2AwpDgttaHubb3fpNejR4/www8/WA3YoAoICACg9Il6UlRU1DN/tHj99dfxyiuvIDQ0FIsWLULHjh1RokQJlChRAh07dsSiRYvSHe/TjnfqMeRpQ7qbTCbcuXMHJUuWfOpyatSogf/7v//DwYMHMWzYsHTFOHr0aCxcuBALFixAxYoV0apVK7Rr1w6tW7dGjhw5kCdPnmSviY6ORtu2bdGtW7cM94cicjVMkohcTN68efHqq69i3rx5Vp33z549i127duG1115DqVKlMvTeCxcuRPbs2TFmzBirKtKTkiZQabn+Sbly5dC5c2dMmTIF0dHR2vQ9e/bgzJkzGDlyJAoWLJihmOfPn49KlSrhhRdesKr22Mqvv/4KANpIUM6OOTo6Gt99912y6Q0bNtQ6yx86dCjZ80k79gOPO5QXKVIEwOMTzh07dlj9b0+fPo3r16+jU6dOVq8vX7481q5di0mTJuGnn37CsmXL0rUeqVHjuHv3brLn7ty5Ay8vL+1EPVeuXPjoo49w6dIl7NixA6GhoRg5cmSya0X5+Phg3bp1+O6773Dx4sVk1y5KbZnqNkrpZPtZ2zM9Vq9ejStXrlhNK1y4MP7v//4PZcqUsfp/zp8/HxEREWjevHm6l5MRtt5v0mvJkiUoUqRIigNgqAN9nD9/3mr67du3ER0djZCQkFTf98cff8Tff/+NCRMmAFCSvkKFCmnPFy5cGP/++2+GYk7teKeOnLd3795UX3vgwAGYTKY0Xd9rwIABeOWVV/Dll1+mO6Hr27cvdu/ejdOnT2PPnj146aWXcPHiRdStW1cb6EJ17949NG/eHG3btsXEiRPTtRwiV8YkicgFDRs2DAaDAR9//LE2Tf0V+ckvuJSa0ajN9NRf3Dds2IC7d+8iPj4eRqPR6sTdFqPEAcCbb76Je/fuYdasWRmOWY1XRPDzzz8jLi5Oi/lZr8+IY8eO4dtvv0V4eDhatmypi5jv3LmDF198UWtilZSa2KZ0Un/s2DGrx3/++SeMRqPWxExdvydHkHvw4AG6dOmSLIEoXbo0vL29MXz4cDRq1AiDBg1KNmyxt7e3tv6XLl3Cnj17nrl+ZcuWRXBwMP7880+r6QkJCfjrr7/QqFEjZM2aFTdv3tR+QTcYDGjUqBF++eUX5M6dG0eOHLF6be7cuREYGIiwsDCMGTMGc+bMsRr+WV33ffv2Wb1OHSmwVatWyeJMaXvmzJkzQ0N2T5s2DWvXrk3xOaPRaPX/tOf+nhJb7zfp9eWXX2LgwIEpPlerVi2UKFHC6n8JQHvco0ePFF93//59DB8+HLNnz9aqagULFrS6SO3t27cz1bwvpeNd69at0bRpU3z55ZepDp89bdo0FChQIM3V2VmzZqFWrVoYOHBgmrf1wYMHtQq5KjIyEocOHUrWzPXWrVto2rQpevfujffee0+b/tprr+HgwYNpWh6Rq2KSROSCihYtip49e2LhwoW4fPkyAOWX/TJlyuCbb77BzZs3ASjDvf7444/JXq9en+Pff//FgwcP0LlzZzx8+BDt27dHVFQUZs+eDQAwm80YN26cTWKuXbs2GjRogGnTpmkn+PXq1UO+fPkwa9YsbdrWrVuTnfSoMd++fRvx8fE4c+YMevXqBS8vL7Rv3x7Hjx/HmjVrAACxsbGZ/rUzPj4eixYtQnh4OGrVqoXly5drz+khZovFgoEDB+L+/fvatKtXr+Kdd95B0aJF0aVLl2Sv+fbbb7VmPkeOHMGCBQvQv39/lClTBgDw3HPPoW7dunj77be1/Sc2NhbDhg2D0WhMsfkboJzEL1iwAHFxcclOsEqUKKH9Gj937lytWdjTGAwGfP755zhy5Ai+/vprAEqCOW7cODx69AhTp04FoFzvZc6cOdixY4f22oMHDyI6OvqpVZaxY8ciJCQEAwYM0JppNWnSBN26dcNnn32m9b27efMmxo0bhwYNGmjX10lq5syZWpVg8+bN+PXXX/HWW28hV65cz1zHlHz88cdWiYbJZML//vc/nDlzRhs6HlAqmps2bdL6zty7dy/FPmi2Yq/9Ji0OHTqEkydPJusPplL3lY0bN2oV3zt37mDChAno1q1bqv2J3n77bTRo0MDqmkhdu3bF6tWrcffuXdy9exdr1qzRLo2QESkd7wBg6dKlyJs3Lzp06IALFy5o06OiojB8+HDs3LkTv/76q1ahexZfX1+sWLECWbNmtapaPc2cOXMwePBg7Qefu3fvon///ujRo4fVNeEiIyPRuHFjBAQEICAgAIsWLdJuO3fuTPPyiFyWs0aMIKLUxcTESEhIiBQsWFAb3a5r165W8xw+fFgASFBQkDRr1kxERE6dOiWtWrWSggULSv369aVXr17St29fbfSlTZs2iYgyYl2nTp2kRIkSUqFCBauhdadMmSIlS5aUsmXLSuPGjWXu3LkCQAoWLJgshqTOnj0rISEhkidPHm15Q4YMsZrnl19+EQBSqlQpeeGFF0REGS64QYMGEhgYKI0aNZKXX35Z2rRpIz4+PhISEiInTpwQEZEbN25IeHi4lC5dWipUqKCNPhUfHy/vvPOOBAUFSZUqVaRp06by2WefadvmyRiSevPNN6VChQra+oWEhEiVKlWkZMmS0r59e1myZEmKQ3I7M+bY2FiZM2eOdO7cWRv1sGzZslK6dGl55ZVX5MqVK1bzq6OUbdmyRdq1ayeVKlWSgIAAGTJkSLIhu6OiomTYsGFSvHhxqVy5soSEhMjo0aO1+Y4ePSohISHi4+MjefLkkbCwMBER6dSpk+TKlUsASKVKlWTZsmXa/7tkyZJStWpVqVu3rvzzzz8yb948bZsHBQVJSEhIslHZRJRhtRs0aCDBwcFSrFgxadWqldXw3zExMTJ+/HipXr26NrJYjRo15LvvvtPmCQsLkzx58mj/l6NHj8rEiROlSJEi2vDY77zzjogoQy+PHz9eSpcuLeXKlZPg4GAZMWKENtS2Sh3d7uDBg9K0aVMpV66cFCpUSMaPH68NvX/58mUJCQmR7NmzS/bs2SUkJOSpo45t27ZNBg8eLKGhoVKlShWpXLmyFC9eXCIiIuTXX3+1mvfBgwfyyiuvSOHChaV69erSsmVLGT9+vPa5+uSTT+S3336TkJAQbb/u0aOHREVFJYspKipKevTooR1nQkJC5LfffrP5frN161ar/SYkJETi4+NT3R6qV199VV566aVnzvfLL79I9erVpUqVKlKmTBl5++23k8Wo2rVrl+TKlUuuXr2a7LlJkyZJhQoVpEKFCvLxxx8/dZkZPd6JKMP4T5s2TcLCwqRKlSracWfUqFHJRi7cv3+/thz185LSMWnz5s3i5eX1zNHtRER++OEHCQsLk+LFi0u1atWkZs2aMmPGjGTvO2LECG0EzZRu27Zte+ayiFyZQeSJHq5EROQ2FixYgBdffBEXLlzQOo5Txo0fPx4TJkxINjiEu+F+Q0Sejs3tiIiIiIiIkmCSRERERERElASTJCIiN9WzZ0+MHTsWANCmTRvMnDnTyRG5toYNG2Lu3LkAlOGnkw7o4U643xARAeyTRERERERElAQrSUREREREREkwSSIiIiIiIkrC29kB2JvFYkFkZCRy5swJg8Hg7HCIiIiIiMhJRATR0dEoXLgwjMbU60VunyRFRkYiKCjI2WEQEREREZFOXLlyBUWLFk31ebdPknLmzAlA2RD+/v5OjcVkMmHjxo1o0aIFfHx8PDYGvcTBGPQTg17iYAyZ46qx6yFuPcSgpzhSoufYHI3bQuGu28Ed10tP6xQVFYWgoCAtR0iN2ydJahM7f39/XSRJ2bJlg7+/v1O/iJ0dg17iYAz6iUEvcTCGzHHV2PUQtx5i0FMcKdFzbI7GbaFw1+3gjuulx3V6VjccDtxARERERESUBJMkIiIiIiKiJJgkERERERERJeH2fZKIiIiIyPWJCBITE2E2mwEo/Vy8vb0RFxenTXMH7rhejlwnLy8veHt7Z/rSP0ySiIiIiEjXEhIScO3aNcTExGjTRASBgYG4cuWKW10L0x3Xy9HrlC1bNhQqVAhZsmTJ8HswSSIiIiIi3bJYLLhw4QK8vLxQuHBhZMmSBQaDARaLBQ8fPkSOHDmeelFQV+OO6+WodRIRJCQk4NatW7hw4QLKlCmT4eUxSSIiIiIi3UpISIDFYkFQUBCyZcumTbdYLEhISICfn5/bJBOAe66XI9cpa9as8PHxwaVLl7RlZoR7bHkiIiIicmvukjCQ/dliX+HeRkRERERElASTJCIiIiIioiSYJBERERER2UH58uXRpEkTq1vp0qXRqFEjq/m+/PJL1KhRA/Xr10e7du0QGRmpPSciGDp0KOrUqYO6devir7/+snrtV199hUGDBj01jsuXL6NJkybw8/NDcHAwmjRpggYNGqB8+fLo168f7t69m+w1e/bsQbt27dCgQQNt/m+//dZqnilTpiA0NBQGgwGLFi2yem7Hjh3aMps2bYoTJ06kGt/SpUtRt25dNGjQAKGhocnea+nSpWjRogWaNWuGWrVqoXv37rh48eJT1zmzOHADEREREZEdBAYGYvv27VbTunXrhvDwcO3xypUrMWHCBBw9ehQBAQGYMGECevXqhUOHDsFoNGL58uXYt28f9u7di9WrV6Nv3744duwYAODBgwf47LPPsHv37qfGUaxYMWzfvh3BwcHo378/xo8fDwCIiopCrVq1MGTIEPzwww/a/MuWLcNbb72FNWvWoEqVKgCA27dvo1u3bjhw4ADmzJkDAHjrrbdQq1YthIeHY8iQIWjSpAmKFi0KAGjcuLG2zK1btyIqKirF2NasWYN+/frh0KFDqFChAs6dO4fQ0FAUKFAALVq0AAA8//zzWLNmDVq2bAmLxYL+/fujVatWOHLkCHx9fdP430gfVpKIiIiIiOxg/vz5Vo/v3r2LTZs2oU+fPtq0iRMnol+/fggICAAADB06FH///Td+/fVXAMDvv/+Oxo0bw2g0okWLFjh+/Dju378PAPjoo4/w2muvaa9NL39/f7Rr1w4bNmzQpt26dQsDBgzAzJkztQQJAAICArBkyRLMnz8fa9assXqf9u3bI1u2bBgwYABEJF0x/N///R9atGiBChUqAABKlSqFtm3b4tNPP9Xm6dixI1q2bAlAGZRh6NChOH36NA4dOpTudU4rJklEHs6wZAnqffABcOuWs0MhIiJKGxHg0SPn3NKRBJQoUcLq8ZIlS9C6dWvkyZMHgJI0/fXXX6hZs6Y2T65cuVC6dGls2bIFAODt7Y3ExEQA0P56e3vjn3/+wW+//YYhQ4ZkalOaTCarC7wuXLgQANCmTZtk8xYqVAhNmjTRKkmqvHnzYsGCBdi8eTP+7//+L13Lv3z5MgoWLJhsObt374bZbAagVLaSUof1jo+PT9ey0oNJEpGHM86ejfzHjsGwfr2zQyEiIkqbmBgY/f2Ru2hRGP39gRw5HHeLiclw2AsWLMCLL76oPb5w4QIAJEsSChQooD3XokULrFu3DrGxsVixYgVq166NHDlyYOTIkfj444/h4+OT4XguXbqEFStW4NVXX9Wm7du3D2XKlIG3d8q9cipUqIADBw4kmx4REYGhQ4finXfewZkzZ9IcQ3BwMC5fvmw17erVq4iPj8ft27dTfM3evXtRuHBh1K9fP83LSS8mSUQeznD1qvL3xg0nR0JEROS+Tp48ievXryMiIkKbFvNfwvVkv5osWbIgNjYWANCyZUu8+uqraNGiBVauXIkffvgBGzduRFxcHDp06ID//e9/aNiwIXr06IGbN28+M44FCxagSZMmqFixIqpWrYopU6Zg0qRJ2vP3799Hjhw5Un19jhw5cO/evRSfmzx5MkqWLIm+fftqVaBnGTp0KLZs2YKdO3cCAA4ePKg1/0vpPeLj4zFlyhTMnj07UwniszBJIvJkiYnA9evKfSZJRETkKrJlgyUqCvf//ReWqCjg4UPH3bJly1DICxYsQN++fa0udJrtv/d6stlYQkICsmbNqj0eOXIkdu3ahTVr1qB48eJ46623MH36dKxbtw7fffcdNm/ejMqVK6ep6V3//v2xfft2HD58GI0aNcK0adNgsVi053Pnzo1Hjx6l+vqHDx8ib968KT7n5+eHH374AYcPH8Ynn3zyzFgAoFWrVlixYgU++ugj1K9fH59//jk++OADGAwG5M6dO9n8r732Gnr27InOnTun6f0ziqPbEXmy69dh+O/AaLh2zcnBEBERpZHBAGTPDpjNyl+jvn/3N5vN+OGHH7Bjxw6r6SVLlgQA3Hjih8qbN28iNDQ0xfeaM2cO6tWrh8qVK2PevHlo2bIlfH190alTJ8ycOTPNMWXJkgUzZsxA2bJl8fPPP6NLly4AgLCwMGzYsAFmsxleXl7JXvf333+jTp06qb5v1apVMWnSJIwZMwZt27ZNUywdOnRAhw4dtMdTp05FyZIltSRSNXr0aGTLlg0fffRRmt43M/S9RxGRff3X1A4AK0lERER2snHjRpQqVQqlS5e2mp4nTx5Uq1YNBw8e1KZFRUXh7NmzaNasWbL3uXv3LmbMmKElCWazWes75OPjow3skFalSpVCp06dMHXqVG1av379ICJYn0Jf5evXr2PHjh0YPHjwU9935MiRaNiwIV544YVnxhAZGZmsD9POnTvRrVs3q2mTJ0/GlStXMHv2bABKs7yk283WmCQRebJ//9XuspJERERkH08O2JDU+++/j4ULF+LOnTsAgFmzZqFChQopji43btw4DBo0SBvyu169eti1axdEBDt27EC9evXSHdvIkSOxd+9e7N27F4AyaMSCBQswbNgwHD9+XJvvzp07eO655zBo0CDt+kWpMRgMWLhwodVFcVOzZ88eDB48WGvyt3nzZhw9ehRvvfWWNs/cuXOxaNEiDBkyBIcOHcKBAwewZs0a7XpR9sDmdkSejJUkIiIiu7p//z62bNmCb7/9NsXnu3Tpgps3byIiIgJ+fn7InTs3lixZYtV3CVAGftiyZQsOHz6sTevevTt27NiBOnXqIEuWLMmuy6S6fPky+vbti+vXr2PBggU4cOAA1q5dCwCoX78+wsLC0Lt3b7Rs2RJffvklunbtiiJFimDMmDG4f/8+vLy8ICJ49dVX8dxzz2nvO2XKFPzwww+4fv06WrRogY0bN2rPFS1aFHPnzsU777zz1O1TsmRJREVFoWrVqggICEDevHmxdetW5MuXDwAQHR2tJVF169a1em1q62sLTJKIPFnSSlJUlDKsaQY7pBIREVFyuXPnTnUoa9XAgQMxcOBAAIDFYkFUVFSyeSpWrIiTJ09aTTMajfjiiy+eGUOxYsWwffv2VJ/ft29fsml16tRJdtHYJ7311ltWFZ8n9ejRAz169LAaGOJJ1atXT3H5qpw5c6Z5pDxbYnM7Ik+WtJIEPB7pjoiIiMiDMUki8mRJKkkAmCQRERERgUkSkWf7r5JkUa+qzcEbiIiIiJgkEXksEa2SFFWsmDKNlSQiIiIiJklEHuvuXeC/K3zfL1VKmcZKEhER6ZSIODsEchG22FeYJBF5qv+qSJI/P2Lz51emsZJEREQ64+PjAwCIiYlxciTkKtR9Rd13MoJDgBN5KnVkuyJFEJcnj3KflSQiItIZLy8v5M6dGzdv3gQAZMuWDQaDARaLBQkJCYiLi0t2TSFX5o7r5ah1EhHExMTg5s2byJ07N7y8vDL8XkySiDyVWkkqXBjxapLEShIREelQYGAgAGiJEqCcEMfGxiJr1qwwGAzOCs3m3HG9HL1OuXPn1vaZjGKSROSp/qskSdGiiMudW5nGJImIiHTIYDCgUKFCKFCgAEwmEwDAZDJh586daNSoUaaaVemNO66XI9fJx8cnUxUkFZMkIk+lXiOpcGHE582r3L9xAzCbARscXIiIiGzNy8tLOwH28vJCYmIi/Pz83CaZANxzvVxxndyjoSMRpV+SSlJ8rlwQg0FJkO7ccXJgRERERM7FJInIUyWpJImXF6COcMfBG4iIiMjDMUki8lRqJalIEeVxwYLKX/ZLIiIiIg/HJInIEz16BNy/r9z/L0kSdRQYVpKIiIjIwzFJIvJE6jWScuYE/P2V+2qSxEoSEREReTgmSUSeSO2PpDa1AyBsbkdEREQEgEkSkWdSK0lFiz6eVqiQ8pfN7YiIiMjDMUki8kSsJBERERGlikkSkSdiJYmIiIgoVUySiDwRK0lEREREqWKSROSJUqokqaPbRUcrQ4QTEREReSgmSUSeKIVKEnLmBLJlU+6zmkREREQejEkSkacxmYAbN5T7SStJBgOvlUREREQEwNvZASxduhTffPMNzGYzoqKiEBwcjClTpiA4OBgA0KRJk2Svadq0KcaOHevYQIncxbVrgAjg4wMEBABm8+PnChUCzp/n4A1ERETk0ZyeJD3//PNYs2YNWrZsCYvFgv79+6NVq1Y4cuQIfH19AQDbt293bpBE7kTtj1SkCGA0WidJrCQREREROb+5XceOHdGyZUsAgNFoxNChQ3H69GkcOnTIyZERuamU+iOpOAw4ERERkfOTpGXLllk99vPzAwDEx8c7Ixwi95fSyHYqVpKIiIiInN/c7kl79+5F4cKFUb9+fW3asGHDcPjwYYgI6tWrh/feew85c+ZM8fXx8fFWCVZUVBQAwGQywWQy2Tf4Z1CX78w49BCDXuLw1BiMly/DC4C5UCFYknwuTCYTDPnzwxuAJTISZgdvF0/9f+gxhoxy1dj1ELceYtBTHCnRc2yOxm2hcNft4I7rpad1SmsMBhERO8eSZvHx8ahSpQo+/fRTdO7cGQAwfPhwtGnTBi1atMDDhw/Rs2dP3LlzB7t374aXl1ey9xg/fjwmTJiQbPrixYuRTR3emMiD1fjsMxTdtQvHBgzA+Q4drJ4rcOAA6k6ciPslS2LHtGlOipCIiIjIPmJiYtCnTx88ePAA/v7+qc6nqySpf//+CAoKwkcffZTqPCdOnEDlypWxceNGREREJHs+pUpSUFAQbt++/dQN4QgmkwmbNm1CREQEfHx8PDYGvcThqTF4hYfDuHs3En/4AdK9u3UMx4/Dp3ZtSKFCSLx0ySHxqDz1/6HHGDLKVWPXQ9x6iEFPcaREz7E5GreFwl23gzuul57WKSoqCgEBAc9MknTT3G706NHIli3bUxMkAChVqhQA4Ny5cykmSb6+vtqoeEn5+Pg4/Z+i0kMseohBL3F4XAyRkQAA7+BgZRjwpDEEBQEADDduwMdoBFKo1tqbx/0/dBxDRrlq7HqIWw8x6CmOlOg5NkfjtlC463Zwx/XSwzqldflOH7gBACZPnowrV65g9uzZAICDBw/i4MGDuHnzJiZNmmQ179X/Op0XK1bM4XESuTwR6yHAn5Q/v3JRWYsFuH3bsbERERER6YTTk6S5c+di0aJFGDJkCA4dOoQDBw5gzZo1OHbsGGJiYjBt2jRcvHgRAGA2m/HRRx+hfPnyaNq0qXMDJ3JFt28DCQlKIqQO952UtzdQoIByn8OAExERkYdyanO76OhoDB48GBaLBXXr1rV6bv78+QgMDMSoUaPQu3dv+Pr64tGjRyhTpgw2bNigDRVOROmgXiOpQAEgS5aU5wkMBG7c4DDgRERE5LGcmiTlzJkTZrP5qfO8++67ePfddx0UEZGbe9o1klSBgcCRI0ySiIiIyGM5vbkdETmQWklKqT+SSm2Gx+Z2RERE5KGYJBF5krRWkgBWkoiIiMhjMUki8iSsJBERERE9E5MkIk/CShIRERHRMzFJIvIkrCQRERERPROTJCJPwkoSERER0TMxSSLyFNHRQFSUcv9plSQ1SXr4ULkREREReRgmSUSeQq0i5coF5MiR+nw5cwLZsyv3WU0iIiIiD8QkichTpKU/kopN7oiIiMiDMUki8hRp6Y+k4uANRERE5MGYJBF5ClaSiIiIiNKESRKRp2AliYiIiChNmCQReQpWkoiIiIjShEkSkadITyWJSRIRERF5MCZJRJ4iPZUkNrcjIiIiD8YkicgTJCQAN28q91lJIiIiInoqJklEniAyUvnr6wvky/fs+dVK0s2bgNlsv7iIiIiIdIhJEpEnUPsjFSkCGAzPnj9/fsBoBCwW4NYt+8ZGREREpDNMkog8QXr6IwGAlxdQoIByn/2SiIiIyMMwSSLyBOkZ2U7FfklERETkoZgkEXmC9FaSACZJRERE5LGYJBF5goxUkjgMOBEREXkoJklEnoCVJCIiIqI0Y5JE5AlYSSIiIiJKMyZJRO7OYrEeAjytWEkiIiIiD8Ukicjd3boFJCYq1z1SE5+0YCWJiIiIPBSTJCJ3p/ZHKlgQ8PFJ++tYSSIiIiIPxSSJyN1lpD8S8DhJevQIePjQtjERERER6RiTJCJ3l5GR7QAgRw7lBrDJHREREXkUJklE7i6jlSSATe6IiIjIIzFJInJ3Ga0kARy8gYiIiDwSkyQid8dKEhEREVG6MEkicneZqSSpSRIrSURERORBmCQRuTORx0lSRipJanM7VpKIiIjIgzBJInJnUVHKEN5A5ipJTJKIiIjIgzBJInJnan+kPHmAbNnS/3oO3EBEREQeiEkSkTvLTH8kgJUkIiIi8khMkojcWWZGtgMeV5Ju3gQSE20TExEREZHOMUkicmeZrSQFBABGozIAxK1btouLiIiISMeYJBG5s8xWkry8gAIFlPvsl0REREQegkkSkTvLbCUJ4DDgRERE5HGYJBG5s8xWkgAO3kBEREQeh0kSkTuzZSWJze2IiIjIQzBJInJXcXHA7dvKfVaSiIiIiNKMSRKRu4qMVP76+SkXk80oVpKIiIjIwzBJInJXSfsjGQwZfx9WkoiIiMjDMEkicle26I8EMEkiIiIij8Mkichd2WJkO8C6uZ1I5t6LiIiIyAUwSSJyV7auJMXEAA8fZu69iIiIiFwAkyQid2WrSlL27EDOnMp9Dt5AREREHoBJEpG7slUlCWC/JCIiIvIoTJKI3JWtKkkAhwEnIiIij+L0JGnp0qVo0aIFmjVrhlq1aqF79+64ePGi9ryI4MMPP0T16tURFhaG559/Hg8ePHBewESuwGx+fJ0kVpKIiIiI0sXpSdLzzz+PUaNGYcuWLdi3bx+yZs2KVq1aIT4+HgAwffp0rFixArt378aff/6JLFmy4IUXXnBy1EQ6d/Omkih5eT1OcDKDSRIRERF5EKcnSR07dkTLli0BAEajEUOHDsXp06dx6NAhmM1mTJ48GYMGDULWrFkBAG+++SbWrFmDY8eOOTNsIn1T+yMFBiqJUmaxuR0RERF5EKcnScuWLbN67OfnBwCIj4/H0aNHcevWLdSsWVN7vkKFCsiePTs2b97s0DiJXIot+yMBrCQRERGRR/F2dgBP2rt3LwoXLoz69etj9erVAICCBQtqzxsMBhQsWBAXLlxI8fXx8fFaUz0AiIqKAgCYTCaYTCY7Rv5s6vKdGYceYtBLHO4cg/HSJXgBsBQqBPMz3jstMRjy54c3AImMRKKdtpc7/z9cLYaMctXY9RC3HmLQUxwp0XNsjsZtoXDX7eCO66WndUprDAYRETvHkmbx8fGoUqUKPv30U3Tu3Bnff/89+vbti5s3byJ//vzafBUrVkS9evXwzTffJHuP8ePHY8KECcmmL168GNmyZbNr/ER6UeH771F2xQqca9cOx19+OdPv53/hAsJHjEBcrlzYsHChDSIkIiIicryYmBj06dMHDx48gL+/f6rz6aqS9Nprr6Fnz57o3LkzAGhJTdLKkPo4tYRnzJgxGDlypPY4KioKQUFBaNGixVM3hCOYTCZs2rQJERER8PHx8dgY9BKHO8fgtXQpACC4Xj0Ua9Mm8zHcvAmMGAHfqCi0adEC8Lb9ocOd/x+uFkNGuWrseohbDzHoKY6U6Dk2R+O2ULjrdnDH9dLTOqmtzJ5FN0nS6NGjkS1bNnz00UfatJIlSwIAbty4gaJJ+lbcuHFDe+5Jvr6+8PX1TTbdx8fH6f8UlR5i0UMMeonDLWP4b4AFr+LF4ZXG931qDP8NAGEwm+Fz7x5QuLCtIk1fHA7CGDLHVWPXQ9x6iEFPcaREz7E5GreFwl23gzuulx7WKa3Ld/rADQAwefJkXLlyBbNnzwYAHDx4EAcPHkTVqlWRP39+HDx4UJv377//xqNHj9C8eXNnhUukf+rodra4RhKgjJBXoIByn4M3EBERkZtzepI0d+5cLFq0CEOGDMGhQ4dw4MABbYhvLy8vjB49Gl988QViY2MBAJ999hnat2+PypUrOzlyIp0Ssf3odgCHASciIiKP4dTmdtHR0Rg8eDAsFgvq1q1r9dz8+fMBACNGjMDDhw9Rv359eHt7o0yZMvjuu++cES6Ra7h/H4iJUe7bslkchwEnIiIiD+HUJClnzpwwm81PncdgMGDs2LEYO3asg6IicnFqFSlfPuC/izDbBCtJRERE5CGc3tyOiGzM1v2RVKwkERERkYdgkkTkbuzRHwlgJYmIiIg8BpMkInfDShIRERFRpjBJInI39qokMUkiIiIiD8Ekicjd2KuSlLS5nYht35uIiIhIR5gkEbkbe1eSYmOB6GjbvjcRERGRjjBJInI39qokZcsG+Psr9zl4AxEREbkxJklE7iQ2Frh7V7lv60oSwH5JRERE5BGYJBG5E7WpXbZsQK5ctn9/DgNOREREHoBJEpE7SdofyWCw/fuzkkREREQegEkSkTuxV38kFZMkIiIi8gBMkojcib1GtlOxuR0RERF5ACZJRO6ElSQiIiKiTGOSROROWEkiIiIiyjQmSUTuhJUkIiIiokxjkkTkTuxdSVKTpFu3AJPJPssgIiIicjImSUTuIjHxcTM4e1WSAgIALy/l/s2b9lkGERERkZMxSSJyFzduABYL4O0NFChgn2UYjUDBgsp9NrkjIiIiN8UkichdqP2RChV6XO2xBw7eQERERG6OSRKRu7B3fyQVB28gIiIiN8ckichd2HtkOxUrSUREROTmmCQRuQtWkoiIiIhsgkkSkbtwVCWJSRIRERG5OSZJRO7CUZUkNrcjIiIiN8ckichdsJJEREREZBNMkojcgYhzKkki9l0WERERkRMwSSJyB3fvAnFxyv3Che27LLWSFBcHREXZd1lERERETsAkicgdqFWk/PkBX1/7LitrViBXLuU++yURERGRG2KSROQOHNUfScV+SUREROTGMpQk7du3D/369UPPnj0BAHPnzsWOHTtsGhgRpYOj+iOpmCQRERGRG0t3kvTzzz+jefPmuHfvHv7++28AQPny5TFmzBj8+OOPNg+QiNLA0ZUkDgNOREREbizdSdJnn32GI0eOYPXq1ciXLx8AoEmTJti0aRO++OILmwdIRGnAShIRERGRzaQ7SfLy8kLJkiUBAAaDQZuePXt2WCwW20VGRGnHShIRERGRzaQ7SYqOjsa1FE6Mjh07hujoaJsERUTpxEoSERERkc14p/cFw4YNQ0hICHr16oUrV65gwoQJOH36NFavXo2vvvrKHjES0bOwkkRERERkM+muJPXt2xfff/89jh49inv37mHWrFmIjIzEqlWr0KdPH3vESERP8+gRcP++cp+VJCIiIqJMS3clCQBatmyJli1b2joWIsoItaldjhyAv79jlqkmSbdvAyYT4OPjmOUSEREROUC6K0nXrl3D6tWrcfjwYW3aH3/8gVOnTtkyLiJKK0f3RwKAfPkA7/9+Y7lxw3HLJSIiInKAdCdJ48aNw7hx4/Cv2gcCwKNHj9C9e3f88ssvNg2OiNLA0f2RAMBoBAoWVO6zyR0RERG5mXQ3t/vrr7+wZ88eZM2aVZvWrFkz7N69G23btkXHjh1tGiARPYMzKkmAMnjD1ascvIGIiIjcTrorSb6+vlYJksrf3x8iYpOgiCgdnFFJAjh4AxEREbmtdCdJCQkJ2LdvX7Lp+/fvR0JCgk2CIqJ0cGYlCWAliYiIiNxOupvbffjhh2jcuDFq166N0qVLAwDOnz+PP/74Az///LOt4yOiZ2EliYiIiMim0l1JatWqFQ4dOoQSJUrgyJEjOHLkCIoXL45Dhw5xWHAiZ3BWJYlJEhEREbmpDF0nqWLFiliwYIGNQyGidDOZHicpjq4ksbkdERERual0V5KeJiIiwpZvR0TPcv06IKJczDV/fscum5UkIiIiclPpriRdunQJ48ePx+HDhxEVFWU1ot11niwROZbaH6lwYeXaRY6UtJIkAhgMjl0+ERERkZ2kO0nq3bs3ChUqhAEDBiBnzpww/HdiJCL49NNPbR4gET2Fs/ojAY8rSfHxwIMHQO7cjo+BiIiIyA7SnSQlJiZixYoVKT6XLVu2TAdEROngrJHtAMDPT0mM7t9XqklMkoiIiMhNpLt9Tvny5REbG5vicxaLJdMBEVE6OLOSBLBfEhEREbmldFeSqlSpgvDwcLRv3x6FCxeGl5eX9tzkyZPRq1cvmwZIRE/hzEoSoCRJp04xSSIiIiK3ku4kaezYsQgMDMQ333yT7LkbN27YJCgiSiNnV5I4DDgRERG5oXQnSXXq1MG2bdtSfC48PDzdASQkJGDs2LGYOnUqzp49i+DgYO25/v3749SpU/Dz89OmVaxYEV988UW6l0PklvRQSQJYSSIiIiK3ku4kae3atak+l1rylJqLFy+id+/eKFu2LMxmc4rz/Pjjj1aJExH9R4SVJCIiIiI7SPfADdmzZ0/1ufReTPbhw4f4/vvv8eKLL6Y3DCK6fRtISFDuq8mKo7GSRERERG4o3ZUkk8mETz/9FOvXr8f169czdTHZypUrAwD+VZsMEVHaqVWkggWBLFmcEwMrSUREROSG0p0kjR49GidPnkS/fv0wffp0jB49GgkJCfjll1/QtGlTmwf4ySef4PTp00hMTERISAjGjh2LggULpjp/fHw84uPjtcdRUVEAlOTOZDLZPL70UJfvzDj0EINe4nD1GAwXL8IbgBQujMRMrEOmtkO+fPABINevZyqGTMdhI4whc1w1dj3ErYcY9BRHSvQcm6NxWyjcdTu443rpaZ3SGoNBkpaC0qBOnTrYvXs3vLy80LRpU2zduhUAYDab0aNHj1QvNPs027dvR3h4OC5cuGDV/+jjjz9G8eLF0bt3b5jNZrz++uvYsmULjh07hhw5cqT4XuPHj8eECROSTV+8eDEvdktupfiGDQidMwfXwsLw57vvOiWGLFFRaN23LwBg9bJlEB8fp8RBRERElBYxMTHo06cPHjx4AH9//1TnS3clKXv27Nq1kRLU/hAAvLy8EBkZmYFQU/dukhM/o9GIadOmIU+ePFiyZAleeeWVFF8zZswYjBw5UnscFRWFoKAgtGjR4qkbwhFMJhM2bdqEiIgI+DjpZFIPMeglDlePwbhvHwCgQLVqaNOmjVNigMUCeeklGEwmtK5eHQgKck4cNsIYMsdVY9dD3HqIQU9xpETPsTkat4XCXbeDO66XntZJbWX2LOlOkuLj4/Hbb7+hVatWKFasGEaMGIFu3bphy5YtuH//fnrfLl38/f2RP39+nDt3LtV5fH194evrm2y6j4+P0/8pKj3EoocY9BKHy8bwXx9Ar2LF4GWD+DO8HQoWBP79Fz537gAlSzovDhtiDJnjqrHrIW49xKCnOFKi59gcjdtC4a7bwR3XSw/rlNblp3t0u2HDhuHbb7/F1atX8f7772PJkiVo2LAh/ve//2Hy5MnpDvRZy0oqPj4ed+7cQbFixWy6HCKX5OxrJKk4eAMRERG5mXRXkrp3747u3bsDAIoUKYLz58/j1KlTCA4ORt68eW0a3Ny5c/HCCy+gZs2aAICJEyciT5482vKJPJqzr5Gk4jDgRERE5GbSnSSprl27hlOnTgEAypUrl6EEKSEhAS1atNCa6fXq1QtBQUFYtmwZAGDq1KkYMWIEvL29ERMTg/z582Pbtm3Inz9/RsMmch96qyQxSSIiIiI3ke4k6e7du3jttdewatUqWCwWAMqgCl27dsWcOXPSlSxlyZIF27dvT/X5IUOGYMiQIekNkcj9RUcDasdDZydJaiWJze2IiIjITaS7T9KLL76IBw8eYNWqVTh+/DiOHz+OlStX4v79+xgwYIA9YiSiJ6lN7fz9gZw5nRsLm9sRERGRm0l3Jenvv//GqVOnYDQ+zq8qVqyINm3aoGLFijYNjohSoZf+SAAHbiAiIiK3k+5KUqlSpawSJJW3t7fVhWCJyI700h8JYCWJiIiI3E66k6SBAwdi9OjRuHjxIiwWCywWCy5evIg333wT/fv3t0OIRJSMXitJIs6NhYiIiMgG0tTczmg0wmAwaI9FBFOmTLGaR0RgNBrRp08f20ZIRMnpqZJUsKDyNyEBuH8fyJPHqeEQERERZVaakqSQkBDMmDHjqfOICEaMGGGLmIjoWfRUSfLzA3LnVhKk69eZJBEREZHLS1OSNGbMGDRu3BgAEBUVBYPBgJwpjKg1ZswY20ZHRCnTUyUJUJrc3b+vNLmrUMHZ0RARERFlSpr6JPXo0UO7nzt3bnTt2vWZ8xGRHempkgRw8AYiIiJyK+keuKFWrVrYuHGjPWIhorRISABu3FDu66mSBHAYcCIiInIL6U6SypUrh+jo6BSfe/XVVzMdEBE9g5qIZMkCBAQ4NxYVK0lERETkRtJ9MdmqVauiSZMm6NSpE4oWLQovLy/tud9//92mwRFRCpL2R0oy6qRTsZJEREREbiTdSdIHH3yAwMBAzJs3L9lzN9QmQERkP3rrjwSwkkRERERuJd1JUp06dbBt27YUnwsPD890QET0DHob2Q5gkkRERERuJd19ktauXZvqc6klT0RkQ3qsJLG5HREREbmRdFeSsmfPjujoaHz99dc4duwYAKWf0ssvv5zitZOIyMb0XEm6exeIjwd8fZ0bDxEREVEmpDtJOnr0KCIiImCxWBAcHAxAqS59+umn2LhxI6pWrWrrGIkoKT1WkvLmBXx8AJNJGZ68WDFnR0RERESUYelubjdixAhMnDgRN27cwP79+7F//37cuHEDEydOxPDhw+0QIhFZ0WMlyWBgvyQiIiJyG+lOkh4+fIhXXnkFRuPjlxqNRrz88st49OiRTYMjoidYLEBkpHJfT5UkgP2SiIiIyG2kO0mKiYlBbGxsitNjYmJsEhQRpeLWLaVJW9LKjV6wkkRERERuIt19ktq2bYuGDRvijTfeQKlSpQAAZ8+exRdffIH27dvbPEAiSkLtjxQYqPQB0hMmSUREROQm0p0kTZo0CUajEYMGDUJ8fDxEBH5+fhgxYgQ+/PBDe8RIRCo99kdSsbkdERERuYl0J0leXl74+OOPMXbsWJw9exYAULp0afj5+dk8OCJ6gh5HtlOxkkRERERuIt1JksrPzw+VK1eGxWLBkSNHUKxYMeTLl8+WsRHRk1hJIiIiIrK7dA/c8Pnnn6Ns2bLYv38/EhMT0bBhQ9SoUQNBQUFYv369PWIkIhUrSURERER2l+5K0rJly/Drr7+iTJkyWLJkCY4dO4bjx48jISEBgwYNQuvWre0RJxEBrlFJun4dEFFG4CMiIiJyQelOkvz8/FCmTBkAwJIlS/DCCy+gYsWK2nNEZEd6riQVLKj8TUgA7t0D8uZ1bjxEREREGZTuJOnBgwd49OgRrly5gg0bNuD333/Xnkvp+klEZEN6riT5+gJ58igJ0vXrTJKIiIjIZaW7T9Jzzz2HQoUKoXr16mjcuDFq1aqFEydOoHfv3ihWrJg9YiQiAIiKAh4+VO7rMUkCOHgDERERuYV0V5KGDx+OevXqITIyUut/5O3tjVatWqFevXo2D5CI/qNWkXLnBrJnd2ooqQoMBE6e5OANRERE5NIyNAR4WFgYLBYLLl26BAAoU6YMypUrZ9PAiOgJeu6PpGIliYiIiNxAupvbxcfH4+2330auXLlQunRplC5dGrly5cI777yD+Ph4e8RIRIC++yOpOAw4ERERuYF0V5Jee+01HDp0CB9//DFKlSoFADh79iy+/fZb3Lp1C/PmzbN5kEQEVpKIiIiIHCTdSdKOHTtw4sQJZMuWzWr6gAEDULVqVZsFRkRPYCWJiIiIyCHS3dyubNmyyRIkAMiRI4dWWSIiO3CFShKTJCIiInID6U6SunXrhhkzZiAhIUGbZjKZMGPGDLRp08amwRFREq5QSWJzOyIiInIDaWpuV7JkSavH169fxzvvvIOCBQsCAG7evAmz2YygoCCMGDHC9lESkWtVku7dA+LjlQvMEhEREbmYNCVJvr6+GD169FPnERFMnDjRJkER0RPi44Fbt5T7eq4k5ckDZMkCJCQoTe6KF3d2RERERETplqYk6fXXX0e/fv2eOd+oUaMyHRARpSAyUvnr5wfkzevcWJ7GYFCqSZcvM0kiIiIil5WmPklDhw5N9bmjR49i9OjRCA4Oxv37920VFxEllbQ/ksHg3Fiehf2SiIiIyMWlewhwALh06RIWL16MxYsX4+TJk/D19UX79u2RJUsWW8dHRIBr9EdScYQ7IiIicnFpTpJu376Nn376CYsXL8Yff/wBo9GIJk2awGw2448//oC/vz8vJEtkL64wsp2KSRIRERG5uDQ1t2vVqhUKFy6sNbv7/PPPcfXqVWzatAmBgYHw9/cHoFxQlojswJUqSWxuR0RERC4uTZWk6OhoAMCYMWPw5ptvInfu3PaMiYiexEoSERERkcOkqZK0e/du/PPPP8iRIweaN2+Ojh074qeffkJcXJy94yMigJUkIiIiIgdKU5IEAMWLF8fo0aNx4MABfPzxxzh69CjCwsJw6tQpbNmyBSKCYcOG2TNWIs/FShIRERGRw6Q5SUqqUqVKmDRpEo4ePYoVK1bgl19+QbVq1bBkyRJbx0dEZvPjqowrVZKuXwdEnBsLERERUQZkaAjwpOrWrYu6devCbDajfv36toiJiJK6eRNITASMRqBgQWdH82wFCih/TSbg7l0gXz7nxkNERESUThmqJKXEy8sLGzdutNXbEZFK7Y9UqBDgnenfNezP1xfIm1e5zyZ3RERE5IJsliQB0IYCJyIbcqX+SCoO3kBEREQuzKZJEhHZgSuNbKfi4A1ERETkwpgkEekdK0lEREREDsUkiUjvWEkiIiIiciinJ0kJCQkYPXo0vL29cfHixWTPf/nll6hRowbq16+Ptm3b4qp6wkjkKVyxksQkiYiIiFyYU5OkixcvonHjxrh27RrMZnOy51euXIkJEyZgw4YN2L17N2rXro127drBYrE4IVoiJ3HFShKb2xEREZELc2qS9PDhQ3z//fd48cUXU3x+4sSJ6NevHwICAgAAw4YNw/Hjx/Hrr786Mkwi5xFhJYmIiIjIwZyaJFWuXBmlS5dO8bm7d+/ir7/+Qs2aNbVpuXLlQtmyZbF582ZHhUjkXA8eADExyn1XSpJYSSIiIiIXptsrU164cAEAULBgQavpgYGB2nMpiY+PR3x8vPY4KioKAGAymWAymewQadqpy3dmHHqIQS9xuEQMFy7AB4DkzYtEb2/ADrHaZTvkywcfALh/H6boaMDPzzlxpBNjyBxXjV0PceshBj3FkRI9x+Zo3BYKd90O7rheelqntMZgEBGxcyzPtH37doSHh+PChQsIDg4GAOzatQuNGjXCn3/+iVq1amnztmnTBgkJCalWk8aPH48JEyYkm7548WJky5bNLvET2Uv+v/5CvQkT8CA4GNtnzHB2OGkngnY9esDLZMLGL79E7BM/dhARERE5Q0xMDPr06YMHDx7A398/1fl0W0lSE5qkVSH1cfbs2VN93ZgxYzBy5EjtcVRUFIKCgtCiRYunbghHMJlM2LRpEyIiIuDj4+OxMeglDleIwXDjBgAgZ/nyaNOmjVNiyChj4cLApUtoWrEipHZtp8WRHowhc1w1dj3ErYcY9BRHSvQcm6NxWyjcdTu443rpaZ3UVmbPotskqWTJkgCAG/+dJKquX7+OiIiIVF/n6+sLX1/fZNN9fHyc/k9R6SEWPcSglzh0HcN/Ax8Yg4JgtHOMNt8OgYHApUvwvn0bSMf76vr/4WExZJSrxq6HuPUQg57iSImeY3M0bguFu24Hd1wvPaxTWpfv9OskpSZPnjyoVq0aDh48qE2LiorCmTNn0Lx5cydGRuRArjiynYqDNxAREZGL0m2SBADvv/8+Fi5ciDt37gAAZs6cicqVK9ut2RGR7rjiNZJUHAaciIiIXJRTm9slJCSgRYsWuH//PgCgV69eCAoKwrJlywAAXbp0wc2bNxEREQE/Pz/kyZMHa9asgdGo69yOyHZYSSIiIiJyOKcmSVmyZMH27dufOs/AgQMxcOBAxwREjnHpErzeeAN569YFWBV8OlaSiIiIiByOJRlyvE8+gXHtWlResMDZkehbbCzwX1NTVpKIiIiIHIdJEjmWyQSsWAEAyPPPP8DZs04OSMciI5W/2bIBuXM7NZQMYSWJiIiIXBSTJHKsrVuB27e1h8alS50YjM4l7Y9kMDg3loxQk6QbNwCLxbmxEBEREaUDkyRyrJ9+AgDIf02xjD/9BIg4MyL9cuX+SABQsKDy12QC7t51bixERERE6cAkiRwnIQFYtQoAYJ49G2Zvbxj+/hs4dszJgemUK49sBwBZsgD58in32eSOiIiIXAiTJHKcjRuB+/eBQoUgbdrgZo0ayvQlS5walm65eiUJ4OANRERE5JKYJJHj/NfUDt27A15e+LdhQ+Xxjz+yyV1KXL2SBHDwBiIiInJJTJLIMWJjgV9+Ue737AkAuFGrFiRHDuDiReCPP5wXm16xkkRERETkFEySyDHWrweio4GgIKBOHQCA2dcX0r698jyb3CXHShIRERGRUzBJIsdQm9r16AEYH+92lv+qSli6FEhMdEJgOpWY+DixcOVKEpMkIiIickFMksj+Hj0C1q5V7vfqZfWUNG8O5M2rXEtn+3bHx6ZXN24AZjPg5QUUKODsaDKOze2IiIjIBTFJIvtbuxaIiQFKlgTUEe1UWbIA3bop99nk7jG1P1Lhwkqi5KpYSSIiIiIXxCSJ7E9tatezJ2AwJH++d2/l74oVQHy84+LSM3fojwSwkkREREQuiUkS2VdUFLBunXJf7X/0pIYNlYrJgwfAb785LjY9c4eR7YDHlaQHD5QRDomIiIhcAJMksq/Vq5XqULlyQNWqKc/j5fU4gWKTO4W7VJJy5QL8/JT7bHJHRERELoJJEtnXjz8qf1NraqdSm9ytXg08fGj/uPTOXSpJBgP7JREREZHLYZJE9nPvHrBxo3I/taZ2qpo1gVKllCZZq1fbPza9c5dKEsAkiYiIiFwOkySyn1WrAJMJqFIFqFjx6fMaDI+rSWxy5z6VJICDNxAREZHLYZJE9pN0VLu0UJOkDRuAu3ftE5MrEGEliYiIiMiJmCSRfdy6BWzZotxPa5JUsaIyuIPJpAwH7qnu3QPi4pT7hQs7NxZbYCWJiIiIXAyTJLKPlSsBsxmoXh0oXTrtr2OTu8dVpICAxyPDuTJWkoiIiMjFMEki+0g6ql169Oql/N2+HYiMtGlILsOd+iMBjytJTJKIiIjIRTBJItu7dg3YsUO536NH+l4bHAzUrav0y1m61OahuQR36o8EPK4ksbkdERERuQgmSWR7y5crSU6dOkrSk16e3uTO3SpJapJ04wZgsTg3FiIiIqI0YJJEtpfeUe2e1KMHYDQCf/4JnDtnu7hchbtVkgoWVP4mJgJ37jg3FiIiIqI0YJJEtnXlCrB7t3Ldo+7dM/YeBQsCTZsq99W+TZ7E3SpJPj7KIBQA+yURERGRS2CSRLa1bJnyt0GDzFVCPLnJnbtVkgAOA05EREQuhUkS2VZmm9qpunQBsmQBTpwAjh3LfFyuxN0qSQCHASciIiKXwiSJbOf8eaUfkdEIdOuWuffKnRto3Vq570nVpJgY5WKygHtWkpgkERERkQtgkkS2ow7Z3aTJ4876maE2ufvxR2W0PE+gVpFy5AD8/Z0biy1xGHAiIiJyIUySyHbUpnbqBWEzq317IHt24MIFYN8+27yn3iXtj2QwODcWW2JzOyIiInIhTJLINs6cAQ4fBry9lf5EtpAtG9Cxo3LfU5rcuWN/JIADNxAREZFLYZJEtqFWkZo3B/Lls937qk3uli4FzGbbva9euePIdgArSURERORSmCSRbdhqVLsntWgB5MmjnFxv327b99YjVpKIiIiInI5JEmXe8ePKUN1ZsgCdOtn2vbNkeTxSnic0uXP3SlJUlDKCHxEREZGOMUmizFOrSC1bKkN325ra5G7FCiA+3vbvryfuWkny9weyZlXu37jh3FiIiIiInoFJEmWOiO1HtXtSo0ZKc63794ENG+yzDL1w10qSwcBhwImIiMhlMEmizDl8GPjnH8DPTxmy2x68vB73dXLnJneJiY+rLO5WSQI4eAMRERG5DCZJlDlqFaltWyBnTvstR21yt3o18OiR/ZbjTNevAxYL4OMD5M/v7Ghsj4M3EBERkYtgkkQZl7Spna1HtXtSrVpAqVJKp//Vq+27LGdRm9oVLgwY3fCjyUoSERERuQg3PBMjh9m/H7h4EcieXakk2ZPB8LjPk7s2uVMHbXC3/kgqVpKIiIjIRTBJooz78Uflb/v2QLZs9l+e2uTut9+Au3ftvzxHUytJ7tgfCWAliYiIiFwGkyTKGIsFWLpUuW/vpnaqSpWAKlUAkwlYudIxy3Qkd68kMUkiIiIiF8EkiTJmzx7lpN7fH2jVynHLVatJ7tjkzt0rSWxuR0RERC6CSRJljDpgQ6dOyvDfjqL2S9q2zf1Otj2lknTjhlKJJCIiItIpJkmUfmYzsHy5ct9RTe1UJUoAdeooI+upzf3chbtXkgoUUAbgMJuB27edHQ0RERFRqpgkUfrt3Kn0K8mTB2je3PHLd8cmdyLuX0ny8QECApT77JdEREREOsYkidJPbWrXpQuQJYvjl9+jh3IdoX37gPPnHb98e7hzB4iPV+4XLuzcWOyJ/ZKIiIjIBTBJovQxmZzX1E4VGAiEhyv31WHIXZ1aRSpQwDmJp6NwhDsiIiJyAUySKH22blWqHvnzP05UnMHNmtwZ1CTJXfsjqZgkERERkQtgkkTpoza169YN8PZ2Xhxduih9XI4fV26uLjJS+euu/ZFUbG5HRERELoBJEqVdQgKwapVy31lN7VR58gCtWyv33aCaZHD3ke1UrCQRERGRC9B9kjR+/HiEhoaiSZMm2q1Lly7ODsszbdwI3L+vVAMaNHB2NI+b3P34ozI6nAszsJJEREREpBtObC+VdjNmzECTJk2cHQapTe26dwe8vJwbCwC0bw9ky6aMcPfnn0Dt2s6OKOPYJ4mIiIhIN3RfSSKdiI0FfvlFue/spnaq7NmBjh2V+y7e5M7g7tdIUrGSRERERC7AJSpJ6REfH4949XozAKKiogAAJpMJJpPJWWFpMST960oxGNasgXd0NCQoCIk1aihDgTshjmRxde8O7yVLID/9hMTJk9NV4dLT/0OtJJkKFsz0ts1oDA7ZDvnywQcAoqNhun9fSXSdEUcqGEPmuGrseohbDzHoKY6U6Dk2R+O2ULjrdnDH9dLTOqU1BoOIvjtzjB8/HufPn8fly5dhMplQunRpjB07FqVKlUp1/gkTJiSbvnjxYmTLls3e4bqtmlOmoMju3finUyec7N/f2eFoDCYTWr34IrI8fIjdH36I21WrOjukdPOKjUW7//pX/bpkCRKzZnVyRHYkgrY9e8I7IQGb5s5FjNr8joiIiMgBYmJi0KdPHzx48AD+/v6pzqf7JGnevHl48OABhgwZAqPRiA8//BAzZszAiRMnUCSFpkkpVZKCgoJw+/btp24IRzCZTNi0aRMiIiLg4+PjOjE8egTvIkVgiIlB4t69kBo1nBNHKrwGDoRx3jxYBgyAee5cp8SQUSaTCXsXLECzwYMh/v5IvH3bKTE4cjt4ly8Pw/nzSNy+HVKvntPiSAljyBxXjV0PceshBj3FkRI9x+Zo3BYKd90O7rheelqnqKgoBAQEPDNJ0n1zuwEDBlg9/uCDDzB37lx88cUXmDRpUrL5fX194evrm2y6j4+P0/8pKj3Ekq4YNmwAYmKAkiXhXbs2YDA4J47UPPccMG8ejKtWwThnDpAli+NjyAS/O3cAAIYiRZwah8O2Q2AgcP48vG/fVq515aw4noIxZI6rxq6HuPUQg57iSImeY3M0bguFu24Hd1wvPaxTWpfvcgM3eHl5ITg4GOfOnXN2KJ5DHdWuZ0+bJkg207ixMiDAvXtKQudisv6XJLn9yHYqDt5AREREOqf7JGnYsGHJpkVGRqJYsWJOiMYDRUUB69Yp9/Uyqt2TvLyAHj2U+y44yp1aSXL7ke1UHAaciIiIdE73SdLq1auxevVq7fE333yDW7duJWuGR3byyy9AfDxQrhyg50ER1AvL/vIL8OiRc2NJJ1aSiIiIiPRF932SJk2ahBkzZmDatGlISEiAr68vNm/ejPLlyzs7NM+g96Z2qrAwoGRJ5cKya9YAvXo5O6I0YyWJiIiISF90nyT16dMHffr0cXYYnunePWDjRuW+XpvaqQwGJTH6+GOlyZ0LJUkeV0likkREREQ6p/vmdm5H3yOuW1u1SrmwaZUqQMWKzo7m2dQmd+vXKwmei/C7e1e54ymVJDa3IyIiIp1jkuRAxq++QrWZM4HERGeHkjZJm9q5gsqVlZvJBKxc6exo0sZkgu/9+8p9T6sk3bwJmM3OjYWIiIgoBUySHOXSJRhHjECxbdvg1aePMhiCnt26BWzZotx3lSQJeFxNcpVR7q5dg0EEkiULEBDg7Ggco0ABpXmk2Qw44eK5RERERM/CJMlRiheHefFimL29Yfz5Z6BjR+UCrXq1cqVyElu9OlC6tLOjSTu1L9K2bS7R58Vw9apyp0gRfQ+MYUve3kD+/Mp9F/gfERERkedhkuRA0rEj9r3/PiRbNuWip61aAQ8eODuslP34o/LXlapIgDLCXe3agMUCLF3q7Gie7b8kSQoXdnIgDqb2S2KSRERERDrEJMnBboWGwrxuHeDvD+zaBTRrpr8mR9euATt2KPfVi7S6EhdqcmdVSfIkar8kDt5AREREOsQkyQmkXj2lOVhAAHDwINC4MRAZ6eywHlu+XBmFr04dIDjY2dGkX48egNEI/PEHcOGCs6N5uv/+7+KpSRIrSURERKRDTJKcpXp1YOdOoHBh4ORJoGFD4OJFZ0elcLVR7Z5UqBAQHq7cV5sN6pTh33+VO56WJHEYcCIiItIxJknOVKEC8PvvQIkSwPnzQIMGwKlTzo3pyhVg925lEIHu3Z0bS2a4SpM7tZLkaX2SWEkiIiIiHWOS5GwlSih9kypUUDrxN2oE/PWX8+JZtkz526CBa1c3unQBfHyAY8eAEyecHU2qtD5JnnKNJBUrSURERKRjTJL0oEgRpeld9erK9YnCw4E9e5wTi6uOavekPHmA1q2V+3qsJl2/DnTvDsPFixCDAVKsmLMjcixWkoiIiEjHmCTpRUAAsHWrUsF58ACIiAA2b3ZsDOfPA/v3K4MedOvm2GXbQ9ImdyLOjUUlAnz7rVI5XL4c4uWFv597Tumb5kk4BDgRERHpGJMkPcmVC/jtN6BFC+VCs23bAr/84rjlq9cVCg8HChZ03HLtpX17IFu2x8mfs/3zjzLk+8svA/fvAzVqIHHPHvzjDglpeqmVpOho4NEj58ZCRERE9AQmSXqTPTuwejXQuTOQkAB07QosXuyYZbv6qHZPyp4d6NhRue/MJncmEzB5MlC1qjL0e9aswNSpyhDl1ao5Ly5nypFDSWABVpOIiIhId5gk6ZGvr1LVeeEFwGwGnn8e+Oor+y7zzBng8GHA21sZ9MBdqE3ufvpJ2ZaOduAAUKsWMGYMEBenNKM8fhwYNUrZ1p7KYODgDURERKRbTJL0ytsbWLAAGDRI6cfy2mtK9cFe1CpS8+ZAvnz2W46jtWypDOJw7ZoyOIajPHqkJEK1awNHjgB58wILFwIbNgAlSzouDj3j4A1ERESkU0yS9MxoBGbPBkaPVh6/9RYwdqx9BiFwt6Z2qixZlCaLgOOa3G3cCFSuDEybBlgsSjXr77+Bvn2VCgopWEkiIiIinWKSpHcGA/DJJ8DHHyuPP/oIGDHCtonS8ePKtYSyZAE6dbLd++qF2uRu+XKln5e93LkD9OunVK8uXgSCgoBff1X6lBUoYL/luipWkoiIiEinmCS5ijFjlKoSAHz+OfDKK7brY6NWkVq1AnLnts176knjxkrV4t49pcpjayJKIlShAvDdd0piO3Sokni2aWP75bkLDgNOREREOsUkyZUMHqz0UzIalWvt9OmT+cqIiPs2tVN5eQE9eij3bd3k7tIlZaj2555TLgRcqZJyIeDPPwdy5rTtstyNWkliczsiIiLSGSZJrqZfP2XkOx8f5W/nzkBsbMbf7/Bh5fo9fn7KdYXcldrk7pdflGtQZZbZDMycqSRF69crTRU/+gg4dAioUyfz7+8J2NyOiIiIdIpJkivq2lW5llLWrMC6dUDr1spFOTNCrSK1bevelY+wMGVUuUePgDVrMvdex44B9esDw4Yp79eggTKC3fvvK8kSpQ0HbiAiIiKdYpLkqlq1UoaTzpkT2LFDGbr77t30vYcnNLVTGQxAr17K/Yw2uYuLAz74AKheHdi3D/D3B+bOVbZ/+fK2i9VTqJWkmzedcw0rIiIiolQwSXJlDRsCW7cq1zX680+gSZP0NV3av18ZhS17dqWS5O7UJnfr1wP376fvtbt2AaGhwMSJQGIi0LEjcPKkcv0qIz9GGVKggLLtLBalPxcRERGRTvDsztXVrKlUMgoVUpqBNWoEXL6cttf++KPyt317IFs2+8WoF5UrK7eEBGDlyrS95sEDYOBAZbuePq1UP5YvB1atAooUsW+87s7LC8ifX7nPfklERESkI0yS3EGlSkqlo3hxZRCGBg2AM2ee/hqLRRn4AXjcDM0TqNWktDS5+/lnoGJF4MsvlcevvKJUj7p25UVhbYWDNxAREZEOMUlyF6VKAb//DpQrB1y5ojTFO3o01dkNe/cCV68q/WpatXJgoE6mJoRbt6Z+Yh4ZqSRCnTsr98uUAbZtA776CsiTx3GxegIO3kBEREQ6xCTJnRQtCuzcqfSduXlTuYjqvn0pzmpYtky506kT4OvrsBCdrmRJoHZtwGKBccUK6+csFuDrr5Xq0cqVgLc38O67ysh1TZo4JVy3x0oSERER6RCTJHdToIBS9ahbVxmcoHlzYPt263nMZhjVPjnuPqpdSv5rcmdQR/YDlOaJTZsCr76q9EOqWRM4cACYNEkZap3sg5UkIiIi0iEmSe4od25g40agWTPg4UPlOkq//qo9HXDyJAzXrytNx5o3d16cztKjB2A0wvjHH8geGQnj5MlA1arKABjZsgHTpgF//AGEhDg7UvfHShIRERHpEJMkd5UjB7B2rTJUdVyc0qzuv8pJkV27lHm6dPHMi58WKqQ1n2s8ahS8xo4F4uOBli2BEyeAESOUkdfI/lhJIiIiIh1ikuTO/PyAZcuAPn2Ua/v07g3j11+j0N69yvOe2NRO9V+TO5/YWEi+fMD33yvXTwoOdm5cnoaVJCIiItIhb2cHQHbm46MkADlzAl9+Ca/Bg+EFQPLnhyE83NnROU+vXrCsWYPLsbEosmABfAoXdnZEnolJEhEREekQkyRPYDQCc+YoidLUqQAAS5cu8PL24H9/jhwwL1+OI+vWoYh6QVNyPLW53cOHyo2IiIhIBzz4LNnDGAzA//4Hc758eDRnDrL+V1EicqocOYDs2YFHj1hNIiIiIt1gkuRJDAZYRo3CtgoV0KZ8eWdHQ6QoVAg4e1YZcZGInMNkApYvh9eaNShvNsPg7Q00aADkyuXsyIiInIJJEhE5V2AgcPasUknKls3Z0RB5lvv3lYtoz5wJ/PsvjADKAcDSpUoLhMqVgfr1H9+Cg5XpRERujkkSETnXf/2SDNevAyVLOjkY8jhRUTBs24YChw4BERHKYDee4MIF4PPPgW+/fdwfsGBBmJ9/HpEHD6Lo5cswnD8PHDum3ObOVeYJDHycMNWrB1Sr5pmXkiAit8ckiYicK+kId0ySyN4SEpSLRW/ZAmzeDOzbB2+zGXUByJw5wPPPA/37KxeYdkd79yoXzF65ErBYlGmVKwMjRwK9e8Pi5YVD69YhsE0b+Ny+DezZo9x27wYOHVI+pytWKDdAudREWNjjpKlePSBvXuetHxGRjTBJIiLn+i9JMty44eRAyC1ZLMDx40pCtHkzsHOnMlBIElKqFOLv3oXfrVvA9OnKrXp14MUXlevMufpJv9kMrFqlJEfqdfIAoEULYNQopYKmNqEzmR4/X6gQ0LWrcgOA2Fhg//7HSdOePcDdu8o23bnz8esqVHicNNWvD5QpwyZ6RORymCQRkXOpw4Bz4Abg7l0Ytm1DoT//VE40eXKZMZcuPU6Ktm4Fbt60fj5/fqB5c+XWrBkSCxfGxjVr0MZohPf33wNr1ihVk0OHlCSiQwclYWrRAnClSydERwPz5inN6i5cUKZlyaJUy0aMUCpI6ZE1K9CokXIDlAT0zBklYVJvZ84Af/+t3L75RpkvIOBxwlS/PlCjhlKBInoWkwl48AC4d0/pP6f+/e++8c4dVDp9GsZ//gFKlVL6zAUHA7lz89hJmeZCR3sicktqJckTk6QHD5Rf4LdtU25HjsBbBGEA8L//KRWMGjWsb+w4n9ydO8r2U5vQnT1r/Xz27MqJvZoYVa6sXD9OZTJBvLwgbdoAnToBt28DixcD8+cDhw8Dy5crt0KFgL59leZ4eh4h9MoVYNYs4KuvlH0MAPLlAwYNUm5qE9fMMhqV7VC+PPDSS8o0tYmemjQdOKBMW71auQFKolajhnXfpgIFbBMT6YuIkqynkOCk+PfJaU9UfZ/kBaA08HjfUvn7P06YUroxiaI0YJJERM7lSZWk6Gjg998fJ0WHDj3uF/IfKV8eDxISkOvKFRju3gU2bVJuqqSJU82ayt/ixT3rCz82VjkBV6tFhw4pJ2MqLy+gdu3HSVHt2ukbXCAgABg6VLkdPgwsWAAsWgRcuwZ8+qlyq1NHqS717KmfYbIPHgQ++0wZmc5sVqaVK6dUjV54wTGjRwYEKJW3Dh2Ux/Hxyv9HbZ63e7dS2du7V7n9d4FzlC5tnTRVqGCdyJLzxMc/O5l52rQnjnEZkjOnktjkyaP8/e++2d8fFy5fRkkfHxgvXwYuXlT2r6go4OhR5ZYSJlGUBkySiMi51F+1b916fGLnLmJilBNDNSn688/k61imDBAertyaNEFivnzYsW4d2jRrBp/Tp5UT3wMHlL/Hjil9QJ5MnPLls6421awJFCvmPl/yZrNyoq0mRbt3KyduSVWqBDRrpiRFjRsrJ0G2EBoKzJihVPbWrlWqS+vXK4M//PEHMGwY0KWLkjA1ber4E3uLRYnrs8+s+wWFhyuDMbRp49xkw9cXqFtXuQFKMnvunHXSdOKEUv07exZYuFCZL08e5TVq0hQW5jkjD9qa2awkDRlJcO7dA+LiMh9DlizJEpyUkp4U/+bKlWozV4vJhBPr1qF4mzYwqvvHo0eAmjCldGMSZTsiyva+c0f5bkrp73/3vW7fRuX8+ZVjkotgkkREzpU/P2A0wmCxwDcqytnRZE5cnHLirCZFf/xh3REeUL5g1aQoPBwoWtT6eXV+X9/HSc+rryrT4uOVROngwcfJ07FjypfQxo3KTaUmTmq1qUYN10mcRIB//lESoi1blH5F9+9bz1OkyONKUdOmQOHC9o0pSxYlGerSRal6LlqkJEwnTypN8xYvBoKCgH79lOZ4pUrZN56YGCWhmD5d2VaAciLZq5dSOape3b7LzyiDQakalS6tbCtAORH/44/HTfT27VOmrVun3ADA2xteoaGoGhAA47ZtSrXQYLC+qe+fnlt6X2Pv+dP4GoPZjKJ79sB44YJSoX5a8zW1yWVm/2+5cqU9wXlymp+f44492bMrlcgKFVJ+nklUymJjn5noJPt7964yYmgaGAH4u9iooUySiMi5vLyU/gjXr8Pv3j1nR5M+CQnKaF9btypJ0d69yX91LVrUOikKDs748nx9laSnZs3H09TESa02qRWnlBKngIDkFaegIH18mV+/rmxHtVp05Yr187lyKdtPTYzKlnVe3IGBwJtvKoM67N+vNMdbskSJeeJE5daokVJd6tYNyJHDdsu+dg34v/8D5sxRTlAA5YTstdeAN95InnS7gjx5gNatlRug/FBw5Ij1gBCRkTAeOIASAPDbb86MVhe8AdRI74uyZk1b5Salaf7+7tP8UQ9JVJ48Nl0lKwkJjxOY9CQ8sbEZX2aWLMoPc/nyKU3CU/ib6O+PE1euoL7t1tTumCQRkfMFBgLXr8NX70lSYqKShKiVot9/V37RT6pgQeVkvmlT5W+pUvY9mU8pcYqLe1xxUpOn48eVDvQbNig3lZo4Ja04OSJxio5WmoepSdHx49bPZ8miNLVSm9DVqKG/keUMBqUZWFiYMrz2zz8rCdPGjY+HxX7jDaB7dyVhatgw49v16FFlGYsXP642liwJDB+uvLctEzFn8/F5vE8PG6ZUFi9fRuKOHTi/ejVKBQfDy2hUpie9AcmnPe1m7/ntuAyLxYLbMTEIKF0axrx5n5305M6tHCvo2RyRROXK9fQkKnt2GMxm5ZgdFZX2ROfOnccXh84IL6+nJjqp/s2W7ZnHNjGZ8ECtDLsInX3jEJFHKlQIOHxYf5Uks1npuK8mRbt2KSf3SQUEAE2aPK4UlS/v/MqMnx9Qq5ZyU8XFKV/YSZvqnTiRcuKUP3/yilPRoplbL5NJaUalJkX79ilJZ1LVqj2uFDVo4JiBBmzFz09p6tarF/Dvv8B33ynN8c6eVRKnBQuUpKZ/f6WZWbFiz35PEaVqMm2ass1U9es/Hprcy8tOK6QjBgNQvDikd2/8nSsXSrRpAy8P759kNpmwd906tEnaF4ccIy1J1KVLqSdRt24pTSCPHFFuKfDOmhUdMlPZMRiUJDm9CY+/v/O/v3SESRIROd9/gzcU3bEDxgkTlF88fXyUykF6/2bkNWr/BosF/hcvwjhrllIF2LEjeV+Y3LmVgQHUSlGlSq7RDMXP73HVQ5U0cUpacbp1Szk5T9qsSU2cklacnpY4iTy+iOuWLcq2fPIXzpIlHydF4eFKwukOihYF3n0XGDNGaSq2YAHw00/A+fPA2LHAuHFKhax/f6WPU9as1q+Pi1P6PE2frvR5ApR9rFs3ZTCG2rUdvUZElFbZswMVKyq3lKQhiTIkTZD8/Z/ZlC3Z31y5POMHFDtjkkREzleiBAAg/7FjSjMxZ/D2hrfRiPAnO6HmzKn0L1ErRSEh7vPlk1LiFBubcsUppcSpQAHrilPx4ii2ZQu8fvxRqbzduGG9vIAAJbn87yKuKFnSMevpLAaDUhFr0EC5oOuKFUp1afv2xxU1f3+gVy8YXngBWe7fh/Gjj4C5c5XtDSj738svK8ORZ6Y/GxHpQxqSKNOVK9i8bx+ad+8OH1eqqLsZJklE5HyDBsFsMuHiX38huGhReFksSvOsxMTU/z7tuaf9TW2Y8cREGAAk+vrC2LgxjGqlqHp1/fWFsaesWZVKRdJqhZo4JR0c4sQJpe39+vXKDYAPgGpPvlfSi7hWreoaVTd7yJ5duRBt377AhQvKyHQLFii/KH/1Fby/+gqtDAYY1H4pQUFKf5yXX9bPdZiIyP6yZwdKlULC6dMc9t7JXOKbf9WqVfj444/h5+cHo9GIL774ApUqVXJ2WERkK/nywfLBBzi+bh2K2bu/gUiqSZYpNhbrDx9G644d2c4/qdQSpyNHrJrqydmzuFesGHJ16QKvli2VC66ys3hyJUoA48crTe+2bwcWLIAsXw5DbCwsNWvC+OabQNeunpWcExHpjO6PwH/++Sf69euHgwcPokyZMvjuu+/QsmVL/P3338iZM6ezwyMiV2MwKL/O+fgk7wtiMkFOnHBOXK4ma1YlCapTR5uUaDJh13+dyT29Y32aGI1K88OmTZE4fTq2L1+OJi++CGOWLM6OjIjI4+m+3cPkyZPRtm1blClTBgDw/PPPIzExEQsWLHBuYERERLbi74+YwECOLEVEpBO6T5K2bNmCmkmu/2E0GlGjRg1sTjocKhERERERkY3ourndnTt3EBUVhYIFC1pNDwwMxP79+1N8TXx8POLj47XHUVFRAACTyQSTegE+J1GX78w49BCDXuJgDPqJQS9xMIbMcdXY9RC3HmLQUxwp0XNsjsZtoXDX7eCO66WndUprDAYRdSgd/bly5QqKFSuGpUuXonv37tr0QYMGYePGjTh79myy14wfPx4TJkxINn3x4sXIxmEUiYiIiIg8VkxMDPr06YMHDx7A398/1fl0XUlSk5qklSH1cWoJz5gxYzBy5EjtcVRUFIKCgtCiRYunbghHMJlM2LRpEyIiIuDjpE7NeohBL3EwBv3EoJc4GEPmuGrseohbDzHoKY6U6Dk2R+O2ULjrdnDH9dLTOqmtzJ5F10lSvnz5kCtXLtx44oKE169fR8lULkLo6+sL3xSGnPXx8XH6P0Wlh1j0EINe4mAM+olBL3Ewhsxx1dj1ELceYtBTHCnRc2yOxm2hcNft4I7rpYd1SuvydT9wQ9OmTXHw4EHtsYjg0KFDaN68uROjIiIiIiIid6X7JGn06NH49ddftf5HP/zwA7y8vNCvXz8nR0ZERERERO5I183tACAsLAwLFixAr169kDVrVhiNRmzYsIEXkiUiIiIiIrvQfZIEAJ07d0bnzp2dHQYREREREXkA3Te3IyIiIiIiciQmSUREREREREkwSSIiIiIiIkqCSRIREREREVESTJKIiIiIiIiScInR7TJDRAAAUVFRTo4EMJlMiImJQVRUlNOuNqyHGPQSB2PQTwx6iYMxZI6rxq6HuPUQg57iSImeY3M0bguFu24Hd1wvPa2TmhOoOUJq3D5Jio6OBgAEBQU5ORIiIiIiItKD6Oho5MqVK9XnDfKsNMrFWSwWREZGImfOnDAYDE6NJSoqCkFBQbhy5Qr8/f09Nga9xMEY9BODXuJgDJnjqrHrIW49xKCnOFKi59gcjdtC4a7bwR3XS0/rJCKIjo5G4cKFYTSm3vPI7StJRqMRRYsWdXYYVvz9/Z2+g+ghBr3EwRj0E4Ne4mAMmeOqseshbj3EoKc4UqLn2ByN20LhrtvBHddLL+v0tAqSigM3EBERERERJcEkiYiIiIiIKAkmSQ7k6+uLcePGwdfX16Nj0EscjEE/MeglDsaQOa4aux7i1kMMeoojJXqOzdG4LRTuuh3ccb1ccZ3cfuAGIiIiIiKi9GAliYiIiIiIKAkmSUREREREREkwSSIiIiIiIkqCSRIREREREVESTJI82O3bt50dQjLOGkdEL9uC46iQu9DLZ4oyTu/HI73HR0SujUmSh/riiy/www8/wGKxODWOgwcP4uTJkzhy5AgAwGAwAHDsl59etgXweP2dFcu5c+eQmJjolGWnxGw2O2W5etgOT342XImePlPpcfr0aVy+fBlXrlxxahyHDx9GXFycU2MAnH88eha9x+cIrnycsDW9fH5tyR3XyZX2WW9nB+CJtm/fjixZsiAuLg5NmzbVpouIdtC3p9mzZ2PdunVYvHgxjEajw5b7pEmTJmHTpk2Ij4/HlStX0KpVK4waNQoVKlSAwWBwSFx62RYrVqzA9evXYbFY0LZtW5QsWdLhMXz22We4fv06PvnkE4cvW/Xbb7/h9u3b8Pf3R926dZE/f36Hx6CH7fC0z4be6eUzlV6TJk3Ctm3bEBkZCW9vb7Rv3x7vvPMO/P39HRrHtGnTcPbsWUyfPt2hy01KD8ejp9F7fI7iyscJW9PL59eW3HWdXGqfFXKojz/+WBo1aiTt2rWTPHnySLt27WTDhg1isVhERLS/9jJr1izp2LGj3L59267LeZZly5ZJs2bNRETkypUrsmbNGsmXL59Ur15d1qxZ45DtoZdtMXHiRGnatKm88sorUr9+fcmSJYtMmTJFLl++7LAYZs6cKR07dpQ7d+6IiIjZbBYR+++PSU2aNEmaNGki7du3l2rVqklAQIAsW7ZMHj586LAY9LAd0vrZ0CO9fKbSa+HChRIRESEiIocOHZLZs2dL9uzZpU2bNvLXX385LA51/7t7966IPN7/1L+OoIfj0dPoPT5HceXjhK3p5fNrS+64Tq64zzJJcqB169ZJkyZNREQkNjZWDh8+LKVLl5ZKlSrJ7Nmz7X5CNn/+fClXrpzVNIvFIlu3bpUlS5bIihUr7LLclHz++efyxhtviMjjE4CLFy9KcHCwVKpUSdavX2/X5etlW/z5558SHh6uPX706JG89957YjAY5LXXXpPTp0/bPYZp06ZJ79695datW6nOk5iYaNcYtmzZIo0bN9Yenz59WgYMGCDe3t4yadKkp8ZmK3rYDiLO/2xklF4+UxkxYcIEmTRpktW0nTt3Sr58+aRRo0Zy7Ngxu8cwY8YM6dmzZ4r7n6NOHvRwPHoavcfnSK56nLAHPXx+bc0d18kV91kmSQ60YMEC6datm4g8Ptm6ceOGNG3aVMqVKydz5syx65fhuXPnJDw8XFavXi0iyk7avXt36dOnj1StWlV8fX2lU6dOcuHCBbvFoK7ftGnTpEmTJhIXFyciIvHx8SIiEhkZKcWKFZPatWvL1atXrV5jS3rYFiIiR48elQYNGsi1a9esfi2eOXOmeHt7y+uvvy6RkZF2WbbFYpG9e/eKwWCQM2fOWE1fuHChjB07Vt5++22HnHjs3LlTOnXqJAkJCVaJiHoCNHnyZG1fsTW9bAd1P58+fXqaPxt6opfPVHqo23zo0KHStWtXbbr6WTx06JDky5dPOnbsaNdj819//SW5c+eWf/75xyqGmTNnyvDhw6VHjx6ybds2MZlMdotBxLnHI3eIzxFc/ThhS+o+4OzPry3p5ZhkS668zzJJcqCVK1dKzZo15fz58yIi2hfezZs3pXHjxlK5cmXZt2+fiNjvl8Nz585JixYtZNGiRTJhwgR5//33RUTkzp07snHjRsmdO7e8+OKLdll2bGysdv/vv/8WLy8v+eijj7Rp6gfm7Nmz4u/vL6NGjbJLHCpnbgvVwYMHpUiRIrJx40YRebwNRJRfXQwGg3z99dciYr8mN59//rn06NFDa9bWrVs3GTp0qHTr1k2qVasmfn5+sm7dOrvG8Ntvv0nevHnl77//FhGxOhkcPXq0ZMmSRTZs2GDXGPSwHURETp065fTPRlqpx6mEhAQRETl//rzTP1MZsWvXLjEYDPLll19q09RkffPmzZI1a1b53//+Z9cYZs2aJd26dZPo6GgREenSpYuMHDlShg8fLhEREeLl5SVffPGFiNhv/ztw4IDTj0dPs3//fl3H50iudJywh6TnSL///rvTP7+Z9eQ5nx6OSZmlrpP6WXTFfZZJkgNFRkZKnjx55PXXX9emJU2UihYtKs8//7xNl3n06FHZs2eP1a/w58+fl+bNm0uHDh0kJiZGRB7vzIsWLZK8efPK8ePHbRrH//3f/0nfvn3lyJEj2jp//PHHYjAYZO7cudp86snW/PnzpXr16nLt2jWbJYzHjx+X/fv3S0xMjPahPXfunMO3xZOef/55KViwoPYLaNL/1Ztvvil58uSx+6+js2fPlnbt2sngwYNl8uTJ2vRLly5J9+7dJXfu3DaP4cn3a9GihVSoUEHrD6TuJ4mJifL8889LmTJl5P79+zaN4UmzZs1y+HYQebyu6n7p6M9GRp04cUJElM+Mug7OOL6k1/nz562qhnFxcfL6669LwYIFZenSpdp0s9ksiYmJMmHCBGnTpo1ER0fbdZvPnDlT2rdvL++8845VUxuTySTDhg2TrFmzyqlTp+y2fBF9HI+SOnfunFUMeovPkVz1OGFrX331lQwePFgePHggIso+MGjQIKd/fjPj3r172vFSRPlfDxw4UAoUKOCy66R+P4iI0877MotJkp09eVBbvHixeHl5yfjx45PNs3XrVilevLicPn3aJjvIlClTpHr16lK0aFEpXbq0/Pvvv9pzly5dknfeeUcePXokFotFW97Jkyelbt26cu3atUwvXzV9+nSpW7eudOjQQb788kvtA3HhwgUZMGCA+Pj4aL+QqnEcPXpUwsPDbXZSPGPGDKlbt65UrlxZihUrJuvXr9f+J47cFiqz2awt59ixY1K1alUpU6aM3Lx5U0QeHzQuX74sNWrUkM2bN9ts2evWrZMffvhB1q5dazV9zpw5EhwcbNXkR0Qp7xcpUkRrRmULkydPljJlysi0adOs4qpQoYK0aNFC67iufjb27NkjlStXtunJ9dq1a2X58uXJ1suR2+Gnn37SvhiTNjO8ePGiwz4bGbVgwQIxGAyyfPlyEVHiU/dbZ3ym0mrGjBnSoEEDqVSpklSrVk1Llv744w9p06aNlChRQn788UcRebzNN23aJI0aNbJpk89NmzbJjh07ZPv27Vb/+7lz50poaKgcOHBARB5/d1y/fl1Kly5tdXKRWcuWLZPPP/9c5s+fry3nyJEjDj0ePc0nn3wiJUuWlPfee0+ioqJExPHHSz1w5eOErU2fPl1q1qwpYWFhMmvWLG199+/fL23atJHg4GCHfH5t6fPPP5eWLVtKkyZNpFWrVrJv3z4xmUzy999/S8uWLV1ynZ78flC52j7LJMlOvv76a63ZTtKmQ1FRUTJu3DgxGAwyduxYq2To2rVr0rJlS5v8CjZr1izp2rWrXLhwQc6dOyfly5eXDz/80GoeNa6kzeBWrlwpERER2klqZi1dulTatGmjfZDVLzPVqVOn5KWXXhKDwSDvvvuutu6//fabtGrVyiZxTJs2TTp06CBXr14Vk8kk9erVk2rVqlkdXByxLaZOnSpTp07VHqtfdhaLRVatWiXlypWTUqVKyaVLl6xe16FDB5t1aJw6dapUr15dmjdvLgaDQebPn2/1/NKlS7WRyZLum82aNZNt27bZJIbPP/9cIiIi5LvvvpNx48Zpn5PExESZNm2alClTRpo1a6ZVlFTNmzeXP/74wyYxTJ8+XRo0aCD9+vUTg8Egb731ltXzjtgOW7ZsEYPBIBEREdoJUNJjxcmTJ+WVV16x62cjM9QvwaT7UdJEST3ptudnKr0+++wz6dixo5w7d05OnTolZcuWtRoIYNu2bdrIozNnztSmr127Vjp16qQ1hcusKVOmSP369aVHjx7i4+MjHTt2lCVLlmjP//zzz8n2fxHlWPDzzz/bJIbp06dLjRo15LnnnhODwSATJ04UEeV/uHLlSoccj55m5syZ0rp1a1mzZo3MnDlTO06o8ZUtW9ap8TmKqx8nbOn777+Xdu3ayaNHj0REtL+qbdu2Sdu2be3++bWlWbNmSfPmzeWvv/6SX3/9VerVqycBAQEyfvx4efjwofz1118ut04iKX8/qFxpn2WSZAf79+8Xg8Eg5cqV0w7sSZODGzduyEcffSTe3t7y4osvyv79+0VEZP369dK0aVO5ceNGppa/c+dOad68udUoSYMHD5aVK1fKP//8Iw8fPtQShOjoaJk3b56sWrVKli9fLs2bN5eTJ09mavlJzZ8/X3744QftscVikV27dsk333wjx48fl0ePHklMTIzMmjVL/P39JSwsTDp37iy1a9e2yegtt2/flqZNm2p9vUREvvvuOyldurTs379fEhMTtf9NVFSUXbaFxWKRmzdvSuXKlZMd6JJWGtesWSM1a9aUPHnyyA8//CAnT56U3377TerUqSNXrlzJdBzTp0+Xrl27SmxsrDx69EgGDRokzz33XIrzPnz4UNsuK1askDp16tikQ+WBAwekTZs2WjOJJ5lMJpkzZ46UK1dOgoOD5ffff5dbt27Jb7/9JmFhYTapQMydO1datmypbftZs2aJwWCQQ4cOJZvXXttBRPm8R0RESJUqVaRFixbaCVDSY8W///4rX3zxhV0+GxmlJo1nzpyRzp07az/6LFy4MNm80dHRMn/+fLsdX9LjypUrUr9+fatt99FHH1n1ExVRRlZ8++23xcfHR5o3by59+/ZN9rrM2LhxozRu3Fj7kWT//v1Ss2ZNqVKlSrLRrO7fv68lmatWrZK6devaZKjrmTNnSrdu3bR97uOPP5bGjRtr/9vExERZt26dVKtWzW7Ho6c5efKktG3bNtWTJYvFIhs2bJDQ0FCnxOdIrnqcsIfp06fLr7/+qj22WCxy9OhRWblypfaj1rlz5+z6+bUVi8Ui8fHx8vzzz8uuXbusnnv55ZelUKFCMnz4cHn06JGcPXtW3nnnHd2vU1LP+n548OCBzJ49W/f7LJMkO9i1a5d06dJFKlSoIGXLlk0xUYqNjZU1a9ZIiRIlpGrVqtKyZUsJCwuzSXOiH3/8UZo2bWo1TW3GVLBgQSlSpIiMHTtWbty4IWazWebNmydhYWHSqVMnm5/AfPrpp9KxY0ftcY8ePaRXr14SFBQkBQsWlD59+mi/BF6+fFk2btwo69ats9k1Lx48eCB16tSR9957T/vFpUGDBtK4cWOZO3euhIeHy+jRo7WTpG+//dYu2+LcuXMSFhYmAwYMkMDAQJkxY4b2XNL94sqVKzJ06FCpXr26NG3aVFq1amWTg8apU6eSVWf+7//+Tz744APZsmWLrFu3zqqJ2/Lly2XYsGEye/Zsady4sTagQmZt3brVKjEzm83y5ZdfyrvvviuTJ0/Wmj4dOHBAunXrJsHBwdK6dWtp1qyZzZravfTSS1YVvd27d0tAQID8/PPPcvbsWe0HBLPZLMuWLbPLdjCbzTJhwgT56quv5KeffpJKlSqlegIkouwXtv5sZFZcXJxERETIxo0bZfDgwWIwGLQfRD777DO5dOmSliTZ6/iSHteuXZOKFSvKvHnztAQlLCxM6tWrJ19//bU0bNhQvv32W+1zcPz4cZk/f74sWrTIJiPyqQnIggULtFGr1Dj++ecf6dKli5QpU0amTJmivWblypXSuXNn+eyzz2y2/126dElatWpl1Zx0+fLl8sYbb8jKlStl3rx52vrevHlTBg8ebPPj0bPs27dPOnTooD1OTEyUqVOnysCBA2XEiBFac7rbt2/LoEGDHB6foyQkJMiHH37o0scJW3r33Xdl8ODBIqIcQ3v37i0dOnSQfPnySUBAgLz//vty7949EbH959deevXqJe+++64kJCRYDTYydOhQCQwMlMmTJ2vHjhMnTrjEOok8+/tB/SFD7/sskyQbM5vNMmPGDJk1a5b8+eefUrFixVQrSiLKqE9Hjx6VvXv3ZvpX8qQfsC+++EJLCiZNmiSvvvqqREdHy+3bt7UTdbU/SFxcnPz777+p/rqfmTjU8vGBAwdk4sSJMnbsWO25SZMmSdmyZWX48OE2b1ebNIaXX35ZypYtK3Xq1JFq1arJwIEDteeGDx8u5cqVk5dfflni4uLEZDLZdFuIKF/wO3fulPnz58vdu3dlyJAhyRKlJ4f2vXr1qty7d89mZeczZ85Injx55MiRI9q0cuXKSUREhNSqVUt8fHykZcuWsnv3bhFR2ju//vrrMmjQIJt2FD906JBERETIgwcPxGw2S5cuXaRfv37y4osvSvbs2aV69eqycuVKbf6TJ0/Kv//+m+nqqio2NlY6dOggbdq0kb1794qISNOmTaVFixaycuVKKVmypLRs2VJr+rR161abbodDhw7JxYsXRUS55su9e/ckPj5e5s2bl+oJkCOuzZQWSWNXv7QHDx4sf/31l0RFRcmwYcPEYDBI165dpXfv3smSf1t+ptIbt/rl27p1aylatKh07txZqlWrJoMGDdLm69KlixQqVEgmTpxoNWqaragJzpYtWyQkJES7IKS6LS9cuCAdO3aU6tWrayM5njlzRt5++2156623bDIE/ZkzZyQ2NlYqVqyotWAQEQkJCZFmzZpJ69atxc/PT2rUqGHVWdzWx6PUqJ+xf//9Vxo0aKAl1d27d5fXXntNPvjgAylZsqSUK1dOPvvsM4fH5wz79++XO3fuuMxxwp6++eYbCQsLk1u3bsn48ePlgw8+EIvFInFxcTJw4EApVKiQVUsNV/DWW29JuXLltGNr0uNm3759JX/+/HLu3DlnhZchFotFEhMT0/z9oGdMkmwk6Un5yZMn5datW2I2m2Xbtm2pJkq2PKhNmTJF5s+fn2L71JQSkB49ekipUqVsfjKgxpG0H0KdOnWkTp06MmTIEKuTdBGRUaNGSZEiRWzarlaNQd3eIiKrV6+WXbt2SdeuXZMdcEaNGiX+/v42/2Xmp59+0jog3r17V/uF8/Tp0ykmSuqoNep9W8Wg/rJ28uRJ7SRix44dMnToUBFR9sM9e/aIv7+/dO7cWUSUg1xMTIzV/zEzMajb4eLFi1K4cGGZOnWq7Nq1S959911tvlOnTknVqlUlPDzcqv+BLSSNYevWrRIYGCjVqlWT+vXrywsvvKDNt3//fgkNDZVatWppncJttR0+/vhjKVq0qPTt2zdZc6D4+HiZP3++dgKUdN/VAzX2/v37WzVNmzlzpowbN05ElH08LCxMDAaDzJs3T0SURNARFwJOjRr3888/r+37X3zxhaxbt07atm2rNdFR9ezZUwoXLqx9Zmxl+vTpUq5cOTlz5oycP39eChcuLK+99po2GIG6n58+fVrKly8vffv21V5rNpttckIxffp0KVu2rNy7d0/OnDmjrfv+/futfrw6ceKEVKlSRRo2bKjN44jhtKdPny7ly5eXEydOyM2bN6VmzZoycuRI2bBhg9Vx4tKlS9KpUyepVauWdsx2p+G+v/nmGxk7dqyMGjUqWfVc78cJW0u6LU6fPi2JiYlSqVIliYiIkPfee09LLFQ9e/ZMdkFrvVm8eLF8+eWXMm3aNLl69apER0dLSEiING7cWPucJ/28V6tWTV566SVnhZsm6jrNmDHD6seczz777KnfD/v27Ut2DNYbJkk2oJ6UJx2+Men48Nu3b9cSJfVL0ZZu374tgYGBEhAQID/++KPVByxpImY2m7WEafv27VKrVi2bjiLyZBzq9rhy5YpUrlxZDAaDfPjhh1aJ2d27d6VBgwY2O5FKLQYR5WS3Ro0aWslX3TbXrl2TkJAQm1ZMZs6cKS1btkz1//3PP/9oidL06dO16bb8JVSN4Vm/4Kv7y/r168VgMFj137JVDEm3w1dffSUGg0Fat26tXedBjeHgwYNiMBhk+/btNo8h6XY4f/683L59W4YPHy5fffWViDz+MUGN4clReTIbQ/v27eXgwYOybNkyq+2h7odPngCJKCOuJW2D7wxPxq7+oKF2oFe/wCdNmiTt27eXQYMGiZeXl/Tv31969eplsypgZuNO+tm6ffu2lC1bVqucqknwgQMHJDQ01KZDSH/++edSr149adWqlXZ9n0WLFonBYJAJEyYk+6Fqy5Yt4u/vb7NRTpPG0LJlSy2GlBILNZYDBw6IwWDQ5rW3pNto06ZNIqIMXmEwGKRevXry9ttvi8jjz8rff/8tRqNRVqxY4ZD4HGXatGnSrFkz+fbbbyUwMFDCwsLk6NGjIvL4GKnX44StJd0WhQoVkho1asiZM2dk165dEhQUJAaDQRYsWKBVLUSUkQ+bNGli9b2vJ1OnTpV69erJe++9J3nz5pVy5crJ+++/L7/++qsEBwdLixYttGNR0j6zr7zyijPDfqon16lixYoyevRoMZvNsnnzZu26eHr7fkgrJkmZ9ORJedIvPPVLSE2U1D5KIsrgCt9//73N4ujevbuUKlVK/Pz85LvvvrNqvpXSF+3cuXOlTZs2yUaHsXUc6oH9jz/+kEqVKklQUJCsWrVKO9H64YcfJDw83KbJ49O2RePGjaVSpUrar1IiIgsXLpQ6deqkOJpURsyaNUs6duyonZQlTVST3v/nn3/kjTfekMDAQFmwYIFs27YtxZMme8VgsVjEbDZrFax///1X6tSpk2zoa1vFoP4foqOjZfjw4WI0GqVjx47adlf30y5dutisX0FqMajr3qpVK3njjTdERNku6rZp1qyZTYYRtlgscv36denSpYtcv379mfPHxsbK/PnzpUaNGlK8eHFp0qSJ09qepyX22NhYefPNN+X555+X3r17a9MbNmwo+fLlc0ofpLTEbbFYpE6dOtKoUSOrKuFXX30ljRs3tlnTwJkzZ0rHjh3FYrHI119/LWXKlNH294kTJ4rRaJSxY8daJXBRUVHSvn17mw2T/mQMajVJxPq7QT0WmEwmSUhIkPDwcG0YcntKaRupvy5PmzZNvLy8pFatWsn6Y/Xv3z9Zh3dXtmzZMmnQoIG2P/7zzz+SNWvWFAdE0dNxwh5S2hZ+fn7aOdOSJUukVKlSEhISIocOHbK6zo49zmtsYe3atRIREaF9B924cUNatmwp3t7e0qdPH1m0aJGUKFFCGjRoIKdPn9Z+tPv888+lf//+YjKZdHPtIFVK69SqVSsxGAwyZMgQiYmJkXfeeUdeeOEF3Xw/pBeTJBtIS4JiMplk27ZtUq1aNfHz85Pw8HCbnIyqidjkyZNl+fLl8sEHH4ivr6/VgfXmzZty9uxZ+eSTT0REGdghPDzcpjtoSnFkyZJFFixYoM2zc+dOCQsLkzJlykhERIR8+OGHKX752TKGJ7fFyZMnpUqVKlK2bFkZMWKETJ06VerXr2+zGGbMmCHNmjVLsRlESk0Kz5w5I++++64YDAapUKGC1QUuHRFD0i+TlStXSsOGDbVmZvaKISYmRi5fvizvvPOOeHl5yciRI7UmkKtXr5ZGjRrZZAS5p8WgJuULFy4Ug8EgX331lZYgrV69WsLCwmzWifTixYvSsGFDq18Ix4wZI927d5e2bdtaddRX9e/fX4oVK+b0L5Gnxd6qVSt59913pX379jJ48GDt19vffvtNKleubPeLnmY07pYtW8qMGTNk3bp1UqpUKalVq5bMnDlTZs+eLQ0aNLDpsaBnz55aovbo0SPp16+frFu3TkSUQWUmT54sBoNBBgwYoCXl6mcgLUl1RmLo37+/FkPSH0+SHgt+/vlnqVOnjt0vypraNlKrInfv3pUpU6aIwWCQ3r17a0nRqlWrpF69errs6J1RU6ZMkU6dOmmP79+/L3nz5pUhQ4bIli1bZOvWrcleo5fjhK2lti3eeOMN2bVrl6xZs0Z+/fVXqVGjhpQoUUJeffVV+fTTT6V27dpOPe48zRdffKENWqSeI965c0cKFSok3t7e8txzz8nWrVslNDRUypUrJz179pRPP/1UqlWr5nLrVLhwYTEYDNKlSxdp3769vPLKK9qPv3r4fkgPJkmZkJaT8ie/6EaPHm2Xg9rPP/+stWN/6aWXxM/PT1asWCGjRo2Sd999Vy5fvixly5aV6tWrS926dW12IvCsOHx9fbU41OFtFyxYIBMnTpSpU6fapDPys2JQt8Xw4cNl4sSJEhkZKb169ZIuXbrIq6++arMPa3R0tLzwwgvJro49fPhw6dWrlwQEBMjw4cO1Jj6qTz/9VMqXL2+TfSKtMezdu1fi4+NlxIgR2sVlmzZtavcYevbsKQUKFJA333xTVq5cKfPnz5ccOXJI9erVpVu3blKtWjWb7JvP2g758uWT4cOHyy+//CLvvfeeGI1GadKkibz00ktSt25dm/+A0LhxY1m2bJmIiDz33HMyYsQIWbp0qbRt21ZKlChh1QflxIkTUr9+fV2c+Dwt9jZt2kiNGjWkfv36Vj8MPXjwwKrfkjM8K+4yZcpIjx49ZPfu3dKyZUtp3ry59OrVy2bHxcjISMmWLZvWZyIxMVEsFosMHTrU6hdVEZFffvlFqlWrJqVLl5bWrVtLjRo1rK5Ub48Y+vTpYzXvnTt3pEuXLjJ//nz59ttvpVmzZnbf/9KzjZYuXSolS5aUsmXLSrt27Wx2nNAD9YdU9dIHY8eOlatXr0rz5s3lhRdekOPHj0ufPn2kYsWKVv0njx8/rpvjhK08a1scO3ZMevfuLSEhIdK/f3+JjY2VKVOmyNtvvy3vvPOOrveJGTNmSKVK/9/enQc1dbVhAH8w0qioaJFNLSiIKNRiGcBhS0J0olawWGURxWrHmTodaZWKYlUWN6y2o3Whi9pata1bsSpqXQKI1o1WaWtRJKCj1gXcABEkkPf7w+E2EYrpJwkB3t+MM8nNzTnPPYNJ3txzT9yF68zqXjNjY2MpOjqaAgICaOfOnaTRaGj16tWUlJRECxcuNMhnpKbyb8f04YcfUnR0NAUHB1NycrLOlzGm8P7wX3CR1AQaK1Dmzp1LRE/ftPPz8w3yoqbRaCg/P1/nm5cZM2aQubk5+fn5Cd8QFhcX04kTJwz27WBjOXx9fY3yK8rPy/DsmZRnV5V7UYWFhbRw4UI6evQo3b9/nyIiIig+Pp4yMzMpKSmJnJycSKFQCPPM79y5Q9OmTWuSD0X6Zujbty8pFAo6dOgQJSUlkZubGykUCqNlSExMJGdnZ1IoFHT79m26fPkyHTx4kPbs2VPvhyENlSEpKYmcnZ1p5MiRdOrUKdq7dy/FxsbSihUrSKVSNVmGupWXJk2aRGPHjqW9e/dSfHy88Hh5eTklJCTQgAEDSKlUEtHTD4+mME/7edlLS0tp/vz5NHDgQDp27JgwVau5PS93WVkZLViwgNzd3YUVDmtra5t8EZvs7Gzas2eP8OGf6OmZEU9PT+F6t7ov2oqLi+ny5cuUk5PTJGeQ9Mnw7PU8qamp5ObmRiEhIUb74K3PGNVtv3HjBp07d46OHz/eZL9VZkquX79O06ZNIw8PD4qKitJ5DyspKaHExERycHAQvoQ1ldcJQ9BnLHr27Klz3aipTUV7VkFBAXXq1InGjBlDubm5RPT0NXTSpEmkVCrpzTffFH4aoKVo7JiOHj1KISEhFBERQUT/TOdtabhIekH6Fih1DLmSR1hYmPBBNzExkVxcXEgsFlNGRgZt2rSpwWk9xsrRoUMHIcfSpUuJyLAvao2Nxddff23QDIWFhcKKPDNmzNB5bMeOHWRjY0MbN24koqcX4xpi/nRjGbZv3062trbCVMjLly83+Wpe+mSwtram9evXN3m/+mbYtm0b2djYGDwD0dOzQ126dCEnJ6d6b4R3796l3r17G+3/53+lT3bt350yFfrkXrZsmUEzaH8oqLu9aNEiYcES7eKguTJo93/z5k2DLC70IvmMMUamou5nKL788ksaN24cEf0zJnfv3iVHR0eKi4trzohGo89YzJo1S9i/JfyNHDp0iCwsLMjZ2ZlGjRpFQUFBwpLlKpWKnJycdK6Xbi3HVFBQ0CKOpSHtwF6ImZkZ+vfvD3Nzc+Tl5QEALC0t0adPH5w7dw5nzpzB5s2bsWzZMgCAlZVVk2fQaDQAAFtbWxQVFWHNmjXIz89HXl4eZs+ejaFDh2L16tUIDg5u8r71zREXFyfkGDNmDICnY2fMDHVjsXbtWoNmcHJywpQpU0BEcHd3BwBUV1cDAMLCwiCRSJCRkQEAMDc3R6dOnYyaITw8HAEBATh69CgAwMXFBd26dTN6BqlUiqysrCbvV98MERERkEgkyMzMNGgGAHBzc8PevXtx//59KJVKHDhwQHjMysoKUVFR6NWrl8Fz/D/0yd6zZ89mTNgwfXI7ODgYNEO7du3q3Q4ICMDKlSuRm5sLkUhkkNeg/5JBu397e3t06dLFoHn+az5jjJGpEIvFaN++PdRqNa5evYqLFy8KY2JlZYWQkBDY2toCAIioOaManD5jYWdnJ+zfEv5GFAoF/vzzT8THxyMyMhJJSUmIiYmBRqOBjY0N/P39YW9vD5FIBKD1HJOtrW2LOJYGNWuJ1grUfbMxffp02rdvH61evZoiIyNJrVbTggULyMzMjDw9PY0yV3b//v3k6OhIkZGRwmov27dvJxcXF6POWzaFHKaQoaSkROdb2brpPLNnz6bFixdzhjaWgYjo8OHD1LlzZ/L29qYNGzYQEdGuXbvIz8/P5H8wsKVmN8XciYmJtHDhQmGVxbaaoTGmns+QLl26RGKxmMaMGSNcw7p7927y8fFpksV9WpLWPhbaP8ExevToVvGDyK3pmLhIaiKm8qE8JSVFOFVbXFxMH3zwgdEv/DOFHKaQoc7NmzeFVbbS09MpMDDQ6Dk4g+lk+OOPPyg4OJhcXFxIKpWSt7d3i7n4uqVmN7XcaWlp5O/v36xTUEwhQ2NMPZ+hHTlyhLp370729vY0bNgw8vHxMemFCQyptY1F3d/0xYsXafny5TRlyhQKCAhoEa+l/6Y1HhMRkRlRKz9nayR3797Fhg0bEBcXB5FIhJKSEixZsgTvvfce+vfvb/Q8tbW1EIlEePLkCcRisdH7N6UczZ0hLy8PkZGRCAwMRF5eHr744gu4urpyhjaaAQAeP36MR48eoby8HN26dTPINFxDaanZTS33uHHj8Omnn8LR0bFNZ2iMqecztGvXruH8+fMQi8UYNGiQyU7JNYbWOBYlJSVYtWoVioqKkJCQgIEDBzZ3pBfW2o6JiyQDaO4P5cz05ObmIicnB8OGDUPfvn05QxvPwNouImr2+fmmkKExpp6PsaZSVVUFIkLHjh2bO0qTaU3HxEUSY4wxxhhjjGnh1e0YY4wxxhhjTAsXSYwxxhhjjDGmhYskxhhjjDHGGNPCRRJjjDHGGGOMaeEiiTHGGGOMMca0cJHEGGOMMcYYY1q4SGKMMcYYY4wxLVwkMcYYYwZWU1OD4uJig/bx999/G7R9xhhrS7hIYoyxNig4OBhisRgODg6IiYkRtp86dQpmZmYoKCgQts2fPx+9e/eGt7c38vLyDJKntLQUMpkMHTp0wKZNmwzSR2OuXr2KpKQknW3z589Hnz59IJPJXqjt27dvY+TIkXjw4MELtfM82dnZeOedd6DRaAzaD2OMtQVcJDHGWBuUnp4OiUSC119/HWvWrBG2K5VKAEBGRoawbfHixRg8eDCysrLg5uZmkDyWlpbIysqCnZ2dQdp/nqtXryI5OVln2+LFizF58uQXapeIMHnyZEybNg2urq4v1NbzjB8/HhYWFlixYoVB+2GMsbaAiyTGGGuj5HI5srOzUVtbK2w7ceIE/Pz8hGIJANRqNdRqNSwsLJojZot26NAhFBUV4a233jJKf7Nnz0ZycjLKy8uN0h9jjLVWXCQxxlgbJZfL8fDhQ5w7dw4AUFVVhZqaGowePRqZmZkgIgDAmTNnMGTIEOzcuRP+/v4ICgqCj48PYmNj8eTJEwBAYmIiunTpAgcHByxZsgQA8NVXX6FPnz5wd3fHlStXAACbN2+Gp6cnJBIJ/P39sXv37kYz1tTUYM6cORg8eDCkUikUCgUuXLgAAFCpVJDJZDAzM8P69esRFhYGDw8PjBgxAvfv39dpZ9GiRXB0dIREIsG7776L8ePHw87ODlOnTkVGRgZmzJgBAJDJZJDJZDh16pTO81esWIFhw4bBxcUFmzdvFrYTEebOnQsvLy/I5XJIJBJs3bpVePzHH39EUFAQzMzM9M787D7h4eEYOHAgwsLCUFlZieTkZEgkEgwaNAjnz5/XyfnKK6+gd+/e2L9/f6Pjyhhj7DmIMcZYm1RTU0Ndu3allJQUIiJSKpU0f/58Onv2LAGg3NxcIiJKTk6mzMxMGjt2LKWnpxMRUXV1NQ0fPpySk5OF9mJiYsjPz0+nj+HDh9ONGzeIiOjnn38mKysrun79OhERqVQqsrCwoJMnTwr7Ozo60jfffCPcnzt3LkkkEqqqqiIiou+//5569OhBZWVlwj4AKCQkhNRqNdXU1JCXlxclJCQIj//www/UtWtXKiwsJCKi06dPk7m5Ob399tvCPpmZmdTQW2JiYiJ17tyZlEolERHt27ePLCwshP63b99Ozs7OVF1dLYyhVCoVnu/u7k7Lli2r1+7zMtftExoaSjU1NVRVVUV9+/YlhUJBBQUFREQ0Z84ckslk9doeMWIExcTE1NvOGGNMf3wmiTHG2iiRSASJRCJcf5SRkYGhQ4fC09MTlpaWwpS706dPw9fXFytXrsQbb7wBADA3N8eYMWNw8OBBob3o6GicPHkShYWFAP5Zba1Xr14AgKVLlyIyMhK9e/cGADg7OyMoKAipqakN5qusrMTKlSsRExMDsVgM4Ol1N1VVVdixY4fOvmFhYWjfvj1EIhECAwORm5srPLZ69WqEhobCyckJADBkyBAMGTJE73GysbGBXC4HAEgkElRUVEClUgnHWFFRgZKSEgBAUFAQli9fLjz3zp07ePnllxtst7HMdcaOHQuRSASxWAwvLy/U1taiX79+AIDAwMB6Z5IAoFu3brhz547ex8cYY6w+LpIYY6wNk8vl+OWXX1BdXS0UQyKRCFKpFEqlElVVVWjXrh3EYjHKysoQFRUFPz8/yGQyrFy5Erdv3xba8vb2xoABA4TpZt999x0mTJggPH7hwgUcPHhQmNImk8lw5coVVFZWNphNpVKhqqoKKSkpOs+xtbWtt1Jcz549hdtdunRBWVmZcP/ixYtCgVTHwcFB7zHSbrtr164AILQ/ceJE2Nvbw9nZGZGRkUhPT4eXl5ewf2lpKdq3b//cdp/NXMfe3l643alTJ537FhYWKC0trfccc3Nzg6+kxxhjrR0XSYwx1obJ5XI8fvwYR44cgbm5uXDGRi6X4/jx4zh27Bj8/PxQUVEBuVwOa2trnDhxAllZWYiPjxeuW6oTHR0tFElpaWn1FiyYOHEisrKyhH8XLlzArl27Gs34ySef6DxHpVJh1qxZOvuIRCLhtpmZWb1cz6q7Rkgf2m3XqWvf2toav/32G9LT02Fubo5x48YhIiJC2K9bt25Qq9XPbfffMj/bd0NZnqVWq//17BVjjDH9cJHEGGNt2GuvvYYePXoIiwHUkcvlKC8vx8cffwy5XI5Lly6huLgYYWFhaNfu6VtHdXV1vfYmTJiAwsJCrFu3Dv3799dZEe/VV19Ffn6+zv6ZmZn4/PPPG8zWr18/dOjQod5z1q5di+zsbL2PceDAgSgqKtLZdu3aNZ37dccEPF0s4t/Obj3r7NmzuH79OoYOHYotW7YgLS0Nu3btwr179wAAdnZ29RaRMLT79+/D1tbWqH0yxlhrw0USY4y1YWZmZpDJZMjJyRGuuwGeFjQ2Njb49ddf4eXlhT59+qBjx47CdUq1tbXYs2dPvfbqVpCbNWsWJk2apPPYvHnzsHfvXvz+++8AgIqKCnz00UcYMGBAg9k6duyImTNnYu3atcL0sYKCAnz22Wdwd3fX+xjff/99/PTTT0KhlJOTU+/6H2trawDAgwcPkJaWhoSEBL3aPnDgANatWyfcV6vV6NGjB7p37w4A8Pf3F65fMhaVSoXAwECj9skYY61O864bwRhjrLmlpqZS165dqaamRmd7eHg4jRo1SriflpZG/fv3Jx8fHwoNDaUpU6aQWCwmuVyu87wNGzZQr169qLa2tl5fW7ZsoUGDBpGvry/5+/vT1q1biYjo4cOHJJVKSSwWk6urK6WmphIRkVqtpvj4eHJ1dSWJRELDhg2jnJwcIiK6desWSaVSAkAeHh6kVCpp1apV5OjoSJaWlhQVFSX0u2jRInJwcCCpVEqxsbEUFRVFU6dO1ckWFRVFgwcPJl9fX7p06RKlpKQIbUVHRwsZ6/o7fPgwnTlzhkaOHEm+vr4klUopMDBQZ7W+I0eOUN++fYWx0CdzQ/vExcWRra0t2draUlxcHCmVSvLw8CAAJJVK6datW0REVFRURBYWFvTo0aP/9kfAGGNMhxnRcyZuM8YYYy1YZWUlNBqNztQ/hUIBqVSKefPmGbz/0NBQhIWF6SxiYShTp06Fh4cHYmJiDN4XY4y1ZjzdjjHGWKumVCoxffp04f6FCxdw6tQphIeHG6X/jRs3Ytu2bfjrr78M2s+3336Lzp076xwrY4yx/w+fSWKMMdaqqVQqzJw5EyUlJXjppZeg0WiQkJAAhUJhtAwajQalpaXCtUqGcO/ePVhZWRmsfcYYa0u4SGKMMcYYY4wxLTzdjjHGGGOMMca0cJHEGGOMMcYYY1q4SGKMMcYYY4wxLVwkMcYYY4wxxpgWLpIYY4wxxhhjTAsXSYwxxhhjjDGmhYskxhhjjDHGGNPCRRJjjDHGGGOMafkffLBO0tsCrH0AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_70.values, linestyle='-', label=\"70% RON 92\", color='red')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 70% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t1C1TKtpFdWd"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 60% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "gAZlQ2GVF0GT",
+        "outputId": "042506a2-c3bb-4bae-ec0f-fbb71862b281"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_60.values, linestyle='-', label=\"60% RON 92\", color='purple')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 60% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pdwf5MvaFfAu"
+      },
+      "source": [
+        "# RATA RATA DATA SPEKTROSKOPI SAMPEL 50% RON 92"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "u4WytIWgF-5G",
+        "outputId": "5dd985fc-a9de-4841-f665-9ffbb99f2e3a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_50.values, linestyle='-', label=\"50% RON 92\", color='brown')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Rata-Rata Data Spektroskopi Sampel 50% RON 92\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sejbGW1KE2cU"
+      },
+      "source": [
+        "# PERBDANDINGAN RATA RATA DATA SPEKTROSKOPI ANTAR SAMPEL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "NFoHvgkkjiok",
+        "outputId": "a0117533-1431-4ff3-d89e-6c8787b7cc81"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum.values, linestyle='-', label=\"100% RON 92\", color='blue')\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_90.values, linestyle='-', label=\"90% RON 92\",color='orange')\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_80.values, linestyle='-', label=\"80% RON 92\",color='green')\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_70.values, linestyle='-', label=\"70% RON 92\",color='red')\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_60.values, linestyle='-', label=\"60% RON 92\",color='purple')\n",
+        "plt.plot(wavelengths_numeric, mean_spectrum_50.values, linestyle='-', label=\"50% RON 92\",color='brown')\n",
+        "plt.xticks(wavelengths_numeric, rotation=45)\n",
+        "# Konfigurasi plot\n",
+        "plt.xlabel(\"Wavelengths(nm)\")\n",
+        "plt.ylabel(\"Absorbance\")\n",
+        "plt.title(\"Perbandingan Rata-Rata Data Spektroskopi Antar Sampel\",fontsize=12)\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Tampilkan grafik\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6yiE3UClWoFh"
+      },
+      "source": [
+        "#DATASET"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MpDH_lBFbqai"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "from sklearn.preprocessing import RobustScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X = df.iloc[:, :-1].values\n",
+        "y = df.iloc[:, -1].values\n",
+        "\n",
+        "# Scaling untuk X\n",
+        "scaler_X = RobustScaler()\n",
+        "X = scaler_X.fit_transform(X)\n",
+        "\n",
+        "# Scaling untuk y\n",
+        "scaler_y = RobustScaler()\n",
+        "y = scaler_y.fit_transform(y.reshape(-1, 1)).flatten()\n",
+        "\n",
+        "# Split dataset menggunakan train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Reshape for Conv1D (samples, timesteps, features)\n",
+        "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+        "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
+        "\n",
+        "# Konversi ke tf.data.Dataset\n",
+        "X_train_ds = tf.data.Dataset.from_tensor_slices(X_train)\n",
+        "y_train_ds = tf.data.Dataset.from_tensor_slices(y_train)\n",
+        "X_test_ds = tf.data.Dataset.from_tensor_slices(X_test)\n",
+        "y_test_ds = tf.data.Dataset.from_tensor_slices(y_test)\n",
+        "\n",
+        "train_ds = (\n",
+        "    tf.data.Dataset.zip((X_train_ds, y_train_ds))\n",
+        "    .shuffle(buffer_size=1000, seed=42)\n",
+        "    .batch(8)\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")\n",
+        "\n",
+        "test_ds = (\n",
+        "    tf.data.Dataset.zip((X_test_ds, y_test_ds))\n",
+        "    .batch(8)\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")\n",
+        "\n",
+        "def inverse_transform_y(predictions):\n",
+        "    return scaler_y.inverse_transform(predictions.reshape(-1, 1)).flatten()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "57AEhF8twvHF",
+        "outputId": "5562ae2f-36d4-48d8-c39a-39e4ebf7d779"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['scaler_y.pkl']"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "# Simpan scaler ke file\n",
+        "joblib.dump(scaler_X, 'scaler_X.pkl')\n",
+        "joblib.dump(scaler_y, 'scaler_y.pkl')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JZJEPTMSmmV1"
+      },
+      "source": [
+        "# Build Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 409
+        },
+        "id": "Kr7LPrQOecOw",
+        "outputId": "24498cf7-e4c3-4f20-9ff6-fb0024ca37b5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: 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__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "data": {
+            "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\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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",
+              "│ Conv1D_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Maxpooling1D (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</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\">64</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Conv1D_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,176</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</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\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,304</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\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Output (<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\">33</span> │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+              "</pre>\n"
+            ],
+            "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",
+              "│ Conv1D_1 (\u001b[38;5;33mConv1D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)              │             \u001b[38;5;34m384\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Maxpooling1D (\u001b[38;5;33mMaxPooling1D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Conv1D_2 (\u001b[38;5;33mConv1D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)               │           \u001b[38;5;34m6,176\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m160\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;34m64\u001b[0m)                  │          \u001b[38;5;34m10,304\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ Output (\u001b[38;5;33mDense\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">18,977</span> (74.13 KB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,977\u001b[0m (74.13 KB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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\">18,977</span> (74.13 KB)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,977\u001b[0m (74.13 KB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "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"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Conv1D(filters=64, kernel_size=5, activation='relu', input_shape=(18,1), name='Conv1D_1'))\n",
+        "\n",
+        "model.add(MaxPooling1D(pool_size=2, name='Maxpooling1D'))\n",
+        "\n",
+        "model.add(Conv1D(filters=32, kernel_size=3, activation='relu', name='Conv1D_2'))\n",
+        "# Flatten layer\n",
+        "model.add(Flatten(name='Flatten'))\n",
+        "\n",
+        "# Fully Connected Layers\n",
+        "model.add(Dense(64, activation='relu', name='Dense_1'))\n",
+        "model.add(Dense(32, activation='relu', name='Dense_2'))\n",
+        "\n",
+        "model.add(Dense(1, name='Output'))\n",
+        "model.compile(optimizer=Adam(), loss=MeanSquaredError(), metrics=[tf.keras.metrics.R2Score()])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "W402P1RI3kG_",
+        "outputId": "eb4414c2-4b5d-4354-c710-c94c5a53087b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from tensorflow.keras.utils import plot_model\n",
+        "\n",
+        "plot_model(model, to_file=\"model_architecture.png\", show_shapes=True, show_layer_names=True, rankdir=\"TB\", dpi=96)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "e5D1N30hiM-1"
+      },
+      "outputs": [],
+      "source": [
+        "callbacks = [\n",
+        "    tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True),\n",
+        "    tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-6, verbose=1)\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tp_crZgVmrdT"
+      },
+      "source": [
+        "# Train Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sLF9u6_ziYml",
+        "outputId": "4e5563b0-7d94-498b-d705-a6d9eafc24da"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 115ms/step - loss: 0.2990 - r2_score: 0.1072 - val_loss: 0.1801 - val_r2_score: 0.4013 - learning_rate: 0.0010\n",
+            "Epoch 2/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1573 - r2_score: 0.5266 - val_loss: 0.0945 - val_r2_score: 0.6857 - learning_rate: 0.0010\n",
+            "Epoch 3/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1053 - r2_score: 0.6829 - val_loss: 0.0641 - val_r2_score: 0.7870 - learning_rate: 0.0010\n",
+            "Epoch 4/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0537 - r2_score: 0.8382 - val_loss: 0.0415 - val_r2_score: 0.8620 - learning_rate: 0.0010\n",
+            "Epoch 5/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0262 - r2_score: 0.9215 - val_loss: 0.0389 - val_r2_score: 0.8707 - learning_rate: 0.0010\n",
+            "Epoch 6/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0144 - r2_score: 0.9572 - val_loss: 0.0339 - val_r2_score: 0.8872 - learning_rate: 0.0010\n",
+            "Epoch 7/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0104 - r2_score: 0.9691 - val_loss: 0.0315 - val_r2_score: 0.8952 - learning_rate: 0.0010\n",
+            "Epoch 8/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0091 - r2_score: 0.9729 - val_loss: 0.0269 - val_r2_score: 0.9105 - learning_rate: 0.0010\n",
+            "Epoch 9/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0081 - r2_score: 0.9754 - val_loss: 0.0265 - val_r2_score: 0.9119 - learning_rate: 0.0010\n",
+            "Epoch 10/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0059 - r2_score: 0.9822 - val_loss: 0.0249 - val_r2_score: 0.9172 - learning_rate: 0.0010\n",
+            "Epoch 11/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0039 - r2_score: 0.9883 - val_loss: 0.0249 - val_r2_score: 0.9172 - learning_rate: 0.0010\n",
+            "Epoch 12/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0025 - r2_score: 0.9925 - val_loss: 0.0263 - val_r2_score: 0.9124 - learning_rate: 0.0010\n",
+            "Epoch 13/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0018 - r2_score: 0.9946 - val_loss: 0.0267 - val_r2_score: 0.9112 - learning_rate: 0.0010\n",
+            "Epoch 14/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0018 - r2_score: 0.9946 - val_loss: 0.0252 - val_r2_score: 0.9160 - learning_rate: 0.0010\n",
+            "Epoch 15/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0019 - r2_score: 0.9944 - val_loss: 0.0242 - val_r2_score: 0.9195 - learning_rate: 0.0010\n",
+            "Epoch 16/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0017 - r2_score: 0.9951 - val_loss: 0.0226 - val_r2_score: 0.9247 - learning_rate: 0.0010\n",
+            "Epoch 17/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0016 - r2_score: 0.9953 - val_loss: 0.0219 - val_r2_score: 0.9270 - learning_rate: 0.0010\n",
+            "Epoch 18/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0012 - r2_score: 0.9964 - val_loss: 0.0221 - val_r2_score: 0.9265 - learning_rate: 0.0010\n",
+            "Epoch 19/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.5417e-04 - r2_score: 0.9971 - val_loss: 0.0222 - val_r2_score: 0.9262 - learning_rate: 0.0010\n",
+            "Epoch 20/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.4076e-04 - r2_score: 0.9978 - val_loss: 0.0224 - val_r2_score: 0.9254 - learning_rate: 0.0010\n",
+            "Epoch 21/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7754e-04 - r2_score: 0.9986 - val_loss: 0.0225 - val_r2_score: 0.9252 - learning_rate: 0.0010\n",
+            "Epoch 22/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.9121e-04 - r2_score: 0.9989\n",
+            "Epoch 22: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.5257e-04 - r2_score: 0.9989 - val_loss: 0.0228 - val_r2_score: 0.9240 - learning_rate: 0.0010\n",
+            "Epoch 23/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7618e-04 - r2_score: 0.9992 - val_loss: 0.0226 - val_r2_score: 0.9247 - learning_rate: 1.0000e-04\n",
+            "Epoch 24/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6660e-04 - r2_score: 0.9992 - val_loss: 0.0226 - val_r2_score: 0.9247 - learning_rate: 1.0000e-04\n",
+            "Epoch 25/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5450e-04 - r2_score: 0.9992 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-04\n",
+            "Epoch 26/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4741e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9247 - learning_rate: 1.0000e-04\n",
+            "Epoch 27/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.1827e-04 - r2_score: 0.9992\n",
+            "Epoch 27: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4525e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-04\n",
+            "Epoch 28/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3936e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-05\n",
+            "Epoch 29/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3877e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-05\n",
+            "Epoch 30/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3822e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-05\n",
+            "Epoch 31/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3768e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-05\n",
+            "Epoch 32/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.0842e-04 - r2_score: 0.9992\n",
+            "Epoch 32: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3715e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-05\n",
+            "Epoch 33/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3650e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 34/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3644e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 35/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3637e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 36/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3631e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 37/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.0767e-04 - r2_score: 0.9992\n",
+            "Epoch 37: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3625e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 38/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3618e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 39/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3612e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 40/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3606e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 41/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3599e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 42/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3593e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 43/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3586e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 44/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3580e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 45/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3573e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 46/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3567e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n",
+            "Epoch 47/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3560e-04 - r2_score: 0.9993 - val_loss: 0.0227 - val_r2_score: 0.9246 - learning_rate: 1.0000e-06\n"
+          ]
+        }
+      ],
+      "source": [
+        "hist = model.fit(\n",
+        "    train_ds,\n",
+        "    epochs=300,\n",
+        "    validation_data=test_ds,\n",
+        "    callbacks=callbacks\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bPdq8xpJmtyi"
+      },
+      "source": [
+        "# Eval Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 923
+        },
+        "id": "XJA-SUsRrnEx",
+        "outputId": "a1a32b50-5883-451e-f1cf-f8581a3a44d9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "47 47\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def plot_history(history):\n",
+        "  train_loss = history.history['loss']\n",
+        "  val_loss = history.history['val_loss']\n",
+        "  train_R2 = history.history['r2_score']\n",
+        "  val_R2 = history.history['val_r2_score']\n",
+        "  print(len(train_loss), len(val_loss))\n",
+        "\n",
+        "  plt.figure()\n",
+        "  plt.title(\"Training & Validation Loss\")\n",
+        "  plt.plot(train_loss, 'b-', label='Train Loss')\n",
+        "  plt.plot(val_loss, 'r--', label='Validation Loss')\n",
+        "  plt.legend(['Train', 'Val'])\n",
+        "  plt.figure()\n",
+        "  plt.title(\"Result R² Score\")\n",
+        "  plt.plot(train_R2,  'g-', label='Train R² Score')\n",
+        "  plt.plot(val_R2, 'y--', label='Validation R² Score')\n",
+        "  plt.xlabel('Epochs')\n",
+        "  plt.ylabel('R² Score')\n",
+        "  plt.legend(['Train', 'Val'])\n",
+        "  plt.show()\n",
+        "\n",
+        "plot_history(hist)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 504
+        },
+        "id": "Tk3WHAzABwzj",
+        "outputId": "7a057b67-af28-4ced-9a0a-c512afb28122"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 586ms/step\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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z52Y5tnLlSnPrrbeaunXrmvDwcFO3bl3zyCOPmPPnz1/xOdu3b29CQkIMYGrUqGHCw8PN0aNHsz23efPmDjVPmjQpyzlxcXFmxIgRpmrVqqZ+/fomPDzcjB071iQlJTmcFxkZaSIiIkxQUJBp3Lixeeihh8zYsWPt/6Yff/yxef31102dOnUMYEJCQsw999xjf/wff/xh+vXrZypXrmzCw8NNu3btzMaNGx1eY+/evaZZs2amdu3apn79+ubzzz+3H9u3b5/p27evqVSpkmnYsKFp1qxZllupjTFmyZIlpk6dOqZSpUqmSZMm5rXXXjMRERH2f8s///zzitf3osTERBMQEGAaNGiQ7fHp06eb6tWrm1q1apmIiAgzZ84cA5jg4GDTt29f8/XXX5vw8HDj6emZ5ectNTXVPProo6Zy5cr2a3nxzqY6deqYV199NcfvOzEx0UycONE0btzYhIeHm/DwcNOkSROzcOHCHL1PKT5sxvyjg1lERETEydSlIiIiInlOgUNERETynAKHiIiI5DkFDhEREclzChwiIiKS5xQ4REREJM9panMgMzOTY8eOUapUKc37LyIikgvGGOLj46lYsWKWGZMvpcCBtTBUSEiIq8sQEREptA4fPkzlypUve1yBg79Xsjx8+LBTF88SEREp6uLi4ggJCbnsqtAXKXCAvRvF399fgUNEROQaXG1IggaNioiISJ5T4BAREZE8p8AhIiIieU6BQ0RERPKcAoeIiIjkOQUOERERyXMKHCIiIpLnFDhEREQkzylwiIiISJ5T4BAREZE8p6nNRURECqG4pDSOnEskPjmdUiU8qBzoi7+Pp6vLuiwFDhERkULm0JkE3t0czaGzifZ9VYN8Gdq6GlWC/FxY2eWpS0VERKQQiUtKyxI2AA6eSWTBlmjiktJcVNmVKXCIiIgUIkfOJWYJGxcdPJPIkXPZH+OrryDxMsfygQKHiIhIIRKfnJ6748bACy9Aly4wfLi17QIawyEiIlKIlCphfXSnZWaSnJpBeqbBw81GCS93PN3c7McBSE6G+++H99+3tsuUgYwM8Mj/j38FDhERkUKkcqAvwf7ebPrjNEmpGfb9Pl7u3FyzLJUDfa0dJ05A796wbRu4u8OsWfDQQ64pGnWpiIiIFDotqwcRUsbHYV9IGR9aVg+yNn78EZo1s8JGYCCsW+fSsAFq4RARESlUjpxL5ItfjtOqRlm617+B1PRMvDzcOJ+Uxhe/HCcs0Ju6d9wBR45A7dqwahWEhbm6bNe3cKSmpjJ27Fg8PDyIjo7Ocvytt96iSZMmtGnThttuu42jR49mefyIESNo2rQpTZo04bHHHiM1NTWfqhcREclf8cnppGcYdh06zzd7Y9iy/wzf7I1h16HzpGcY4jNs1piNXr1g69YCETbAxYEjOjqaiIgIjh8/TkZGRpbjy5YtY9KkSaxdu5bNmzfTokULevToQWZmpv2cMWPGsHfvXrZt28b27dvZvXs3Y8aMyc+3ISIikm8cBoX+xSslmdD9v/59vG1bWLECAgLyubrLc2nguHDhAosWLWLYsGHZHp8yZQpDhgyhbNmyAIwYMYJff/2VL774AoAzZ84wZ84cRo0ahbu7O+7u7owaNYo5c+Zw9uzZfHsfIiIi+aVyoC9Vg3zt24FnT/LU8w8w5qVHaBZ/5O9BowWMSwNH/fr1CbtMU8/Zs2f56aefaNq0qX1fQEAAtWrVYv369QBs3LiRtLQ0h3OaNWtGWloa3377bd4WLyIi4gL+Pp4MbV2NqkG+VN//C/+dPJRqB/eQ4eVN7zD/ArueSoEdNBoVFQVAcHCww/4KFSrYjx04cAAPDw+CgoLsx8uVK4e7u7v9nOykpKSQkpJi346Li3Nm6SIiInmqSpAfT5zajvf0h3BLSSG5Tl0yl62gQu2ari7tslw+aPRyEv+aftXb29thv7e3t/1YYmIiXl5eWR7r5eVlPyc7zz//PAEBAfavkJAQJ1YuIiKShzIzYexYfP41HLeUFE527MYvH39BUsWC/VlWYAOHr6/VB3VpS8TF7YvHfH19s70jJTU11X5OdsaNG0dsbKz96/Dhw06sXEREJA/Nm2dNVQ7MbzeQHu1G8X8r/2Dsskh+Pxbr4uIur8AGjurVqwNw8uRJh/0nTpywH6tevTrp6emcOXPGfjwmJoaMjAz7Odnx9vbG39/f4UtERKQwONVvELvC2/J0v7HM7jwM42Z9lEcejuWldXs5FZfs4gqzV2ADR2BgII0aNWLnzp32fXFxcezbt4/OnTsD0K5dOzw9PR3O2bFjB56enrRr1y7faxYREckTP/4Iaday8wfOp/CvO8aztkHHLKdFHo7lwOmE/K4uRwps4AD473//y3vvvWdvwZg1axb169fn1ltvBSAoKIgHH3yQmTNnkpmZSWZmJjNnzuTBBx+kTJkyrixdRETEOd55B1q2hJEjAYhNSgOb7bKnxyal5VNhuePSu1RSU1Pp2rUr58+fB2DAgAGEhITw6aefAnDHHXdw6tQpunTpQokSJQgMDGTVqlW4uf2dk6ZPn84TTzxBs2bNAGjdujXTp0/P9/ciIiLiVOnp8MQTMHOmtX36NKSnE3CV216vdtxVbMYY4+oiXC0uLo6AgABiY2M1nkNERFzv/HkYMADWrrW2J0+G//4XbDZOxSUzdlkkkYezDhBtEBLAtDsaUN6/RL6VmtPP0ALdpSIiIlLs7NtndaGsXQu+vrBkCYwfb+9GKe9fgjFdb6RBiOO05Q1CAhjT9cZ8DRu5UWAn/hIRESl2UlOhSxc4dAhCQmDlSmjUKMtpdStaLRkHTicQm5RGgI8n1cv6FdiwAQocIiIiBYeXF7zxBkybZrVsVKhw2VPL+5co0AHjn9SlIiIi4kppabBnz9/bPXrAxo1XDBuFkQKHiIiIq5w5A127ws03w6VrgLkVvY/noveORERECoPffoPmzeGbbyA5GQ4ccHVFeUqBQ0REJL998QW0amWFjNBQ2LoVOnVydVV5SoFDREQkvxgDL70EPXtCfDxERMD27VCvnqsry3MKHCIiIvll3jxr9lBj4IEHYN06KFvW1VXlCwUOERGR/DJ4sNWV8tprMGeOdRtsMaF5OERERPLS/v3WOA03N/DxgU2bwN3d1VXlO7VwiIiI5JVly6BBA2stlIuKYdgABQ4RERHnMwaefRb69oXERPj+e2v112JMgUNERMSZEhNh4EB45hlre8QI6zZYj+I9iqF4v3sRERFnOnoUevWCnTutgDF7Ntx/v6urKhAUOERERJwhJQXatoXoaAgKgqVLrXk2BFCXioiIiHN4e8PEiVC/Pvzwg8LGPyhwiIiIXKvMTKsb5aIhQ6zulNBQ19VUQClwiIiIXIsLF6y7UFq3hlOn/t5fjCbzyg0FDhERkdw6eBDatIEVK+DECdixw9UVFXgKHCIiIrnx3XfQrBlERkJwMHz7Ldx6q6urKvAUOERERHLq3XehY0eIiYFGjazBoS1burqqQkGBQ0REJCfeeQeGD4e0NGvsxqZNEBLi6qoKDQUOERGRnOjbF268ESZMgE8+AT8/V1dUqGjiLxERkcs5dQrKlQObDUqXhh9/BF9fV1dVKKmFQ0REJDtffw21a8Prr/+9T2HjmilwiIiI/NObb0LXrnDuHCxeDBkZrq6o0FPgEBERuSgtDR5+GB56yAoZd98N69eDu7urKyv0NIZDREQE4OxZuPNOqyvFZoOpU+Gpp6zv5bopcIiIiCQnW1OU790LJUvCBx/A7be7uqoiRV0qIiIiJUrAAw9AtWqwZYvCRh5Q4BARkeLJGDh//u/tUaPg55/hpptcVlJRpsAhIiLFT0oK3HeftQBbXJy1z2YDf3/X1lWEKXCIiEjxcuoUdOpkrYuyZw9s2ODqiooFBQ4RESk+IiOtlV43b4aAAPjf/6BXL1dXVSwocIiISPGwYoV1J8qhQxAWBlu3Qrdurq6q2FDgEBGRom/RIujTBxISrO6Ubdusacsl3yhwiIhI0delC1SuDI88AqtXQ5kyrq6o2NHEXyIiUjQlJPy9hHyFCvDTT1C2rGtrKsbUwiEiIkXPjh1w443w4Yd/71PYcCkFDhERKVoWL4abb4ajR2HGDK30WkAocIiISNGQmQnPPAMDBlhro9x6qzXHhlZ6LRA0hkNERAq/hAS4915YtszaHjMGpk1T2ChAFDhERKRwS0qCtm1h1y7w8oK5c2HIEFdXJf+gwCEiIoWbjw/ccgscOwbLl1uTe0mBozEcIiJSOKWk/P39c89ZLRwKGwWWAoeIiBQuGRnw5JPQvr01OBTAzQ1uuMGlZcmVqUtFREQKj7g4GDQIvvjC2v7f/+COO1xbk+SIAoeIiBQOBw5Az57w++9QooS1vLzCRqGhwCEiIgXfN99Av35w5gxUrAgrV0LTpq6uSnJBYzhERKRg++QTa/G1M2egWTP44QeFjUJIgUNERAq2xo3B3x8GDoRvv7VaOKTQUZeKiIjkq7ikNI6cSyQ+OZ1SJTyoHOiLv4+n40np6eDx10dUWJi1GFu1amCz5Xu94hwKHCIikm8OnUng3c3RHDqbaN9XNciXoa2rUSXor6Xk9+6FPn3g5ZetCb0AQkNdUK04k7pUREQkX8QlpfHu5mj+OHWBc4mpnIpP4VxiKvtOXmDBlmjiktJg3Tpo0QJ277bm2sjMdHXZ4iRq4RARkXxx5Fwivx2LZX9MAnHJafb9/iU8SU1LJ3H6DPwnPW2FjDZtrIXY3PT/xUVFgf+XTE1NZdKkSbRs2ZJWrVrRtm1bduzYYT9ujGHy5Mk0btyY5s2bM3jwYGJjY11YsYiIZOdMQmqWsAGQlJDIwHeeo8KEcVbYGDYMvvoKypd3UaWSFwp84Bg3bhxLlizhyy+/5Pvvv+eRRx6ha9eunDp1CoBXXnmFpUuXsnnzZrZv346Xlxf33HOPi6sWEZF/skGWsOGdlsKCD/9Ln52rMW5uMGMGvPMOeHu7pkjJMwU6cGRmZjJnzhyGDx9OqVKlABgwYADe3t689dZbZGRkMG3aNB566CF8fHwAGDNmDKtWreKXX35xZekiIvIP7m426t5QymFfiocXB4IqkVDCjz1z34fHH9edKEVUgQ4cp0+fJjExkeDgYIf9FSpUYOPGjURGRhITE0PTSyaAqVOnDn5+fqxfvz6/yxURkStwc7PRtd4N1L2hFDbz12BQm41PhzzJ8rdXEtexq2sLlDxVoAeNlitXDj8/Pw4dOmTfZ4zh+PHjJCcnc+DAAQCHQGKz2QgODiYqKuqyz5uSkkLKJcsax8XF5UH1IiJyqdAgP97ZtJ8Hty6lzp+7WDFxNh5enhw5m8jGpFS6XrwtVoqkAt3CYbPZeOSRR5g7dy5Hjx4F4M033+Ts2bNkZGSQmGjdx+39j74+b29v+7HsPP/88wQEBNi/QkJC8u5NiIgIAOW94MWVM+j+watU2/Ytxxd8xILN0Zy6kMLITrUo71/C1SVKHirQLRwAzz33HEFBQdx1113YbDYiIiIYOHAge/bswdfXF8ChteLi9sVj2Rk3bhyPP/64fTsuLk6hQ0QkL504Ab17U3rbNoy7O9HPPE/HvvfQx8eT6mX9FDaKgQIfONzd3XniiSd44okn7Pt69OjBTTfdRPXq1QE4efIklStXth8/efKk/Vh2vL29s7SKiIhIHvnxR+jVC44cgcBAbJ9+SminTmju0OKlQHepAERGRnL27Fn7dlpaGlu3bqVfv340aNCAcuXKsXPnTvvx3bt3k5CQQOfOnV1RroiIXGrVKmjb1gobN94I27ZBp06urkpcoMAHjrlz5/L666/bt6dMmUKzZs245ZZbcHd3Z+zYscyePZukpCQAZsyYQc+ePalfv76rShYRkYtCQqzbXLt1g61boWZNV1ckLlLgu1RatGjB1KlT+eKLL/D09KRJkyYsWbLEfnzUqFFcuHCBNm3a4OHhQc2aNVm4cKELKxYRKeaM+XsujYYNYfNmqF//79VfpViyGWOMq4twtbi4OAICAoiNjcXf39/V5YiIFF6HD8Ndd1krvbZs6epqJB/k9DNUcVNEJAfiktI4ci6R+OR0SpXwoHKgL/4+nq4uq2DZuhV694aTJ+GBB2DXLi2+JnYKHCIiV3HoTALvbo7m0Nm/5/epGuTL0NbVqKLJqiyLFsH990NKCtx0E3z2mcKGONBPg4jIFcQlpWUJGwAHzySyYEs0cUlpl3lkMZGZCWPHwr33WmGjVy/YsgWqVXN1ZVLAKHCIiFzBkXOJWcLGRQfPJHLk3OVnNS7ykpKsLpQXXrC2//MfWLYMSpZ0aVlSMKlLRUTkCuKT06/reJHm7W3deeLtDfPnw6BBrq5ICjAFDhGRKyhV4sp/Jq92vEhzc7PGbuzdC40bu7oaKeDUpSIicgWVA32pGpT92kxVg3ypHHj5dZuKpHnzYOhQa64NAD8/hQ3JEQUOEZEr8PfxZGjrallCx8W7VIrNrbHp6TBypHUnynvvwfLlrq5ICpli3BYoIpIzVYL8GNm5VvGdh+P8eWsyr3XrrO3Jk6FPH5eWJIWPAoeISA74+3hS1yfA1WXkv3374PbbrXEavr6wcCH07evqqqQQUuAQEZHsffUV9OtntXCEhMDKldCokaurkkJKYzhERCR7np5w4QK0agXbtytsyHVRC4eIiGSvXTv48ksrcHh7u7oaKeTUwiEiIpYzZ6ypyX/77e997dsrbIhTqIVDRETg99+hZ084cAAOHoQff9Tia+JU+mkSESnuvvgCWra0wkZoKHzwgcKGOJ1+okREiitj4KWXrJaN+HiIiLAGh9ar5+rKpAhS4BARKY5SUmDYMHjiCSt43H+/NbFX2bKurkyKKAUOEZHiyM0NDh2y/jtrFrz1Fnh5uboqKcI0aFREpDjy9IRPP4Wff4aOHV1djRQDauEQESkuli6Fp576ezsoSGFD8o1aOEREijpjYMoUeOYZa7tNG2t9FJF8pMAhIlKUJSbC8OGweLG1/dhjcOutrq1JiiUFDhGRouroUejdG3bsAA8PmD3buhtFxAUUOEREiqIffrCmKT9+3BqrsXSpNc+GiIsocIiIFEXHj8OJE9YkXqtWWTOIiriQAoeISFF0++3Wba9duoC/v6urEdFtsSIiRcKFC3DffdbCaxf17auwIQWGWjhERAq7gwetFo3ISPj1V9i6FWw2V1cl4kAtHCIihdl330GzZlbYCA6GV19V2JACSYFDRKSwmj/fmik0JgYaNbLuTGnZ0tVViWRLgUNEpLDJyIDRo60xG2lp0K8fbNoEISGurkzkshQ4REQKm5QU+OYb6/uJE61ZRP38XFmRyFXlOnCsWbOG4cOH89tvvwHw1FNPERAQQLNmzdi3b5/TCxQRkX/w9YWVK63JvCZMsJaYFyngbMYYk5sHdO7cmcGDBzNgwAC2bdtGx44dee2110hNTWXt2rWsXr06r2rNM3FxcQQEBBAbG4u/biETkYLo669h1y54/HFXVyLiIKefobm+LdYYw9ChQwFYtGgRvXv35qGHHgJg+fLl11atiIhc3ptvwqOPWmM36teHrl1dXZFIruW6HS4xMRGwEs3SpUsZMmSI/ZhNt2KJiDhPWho8/DA89JAVNgYPhnbtXF2VyDXJdQtH/fr16dChA+fOnaNs2bL06NGD8+fP8/HHH+OmfkQREec4exbuvNPqSrHZ4Pnn4cknNceGFFq5Tgivv/463bt3p3379qxevRo3Nzd+/PFHtm3bxlNPPZUXNYqIFC+7d0Pz5lbYKFkSVqyAp55S2JBCLdeDRosiDRoVkQLlvfdg6FCoVg0++wxuusnVFYlcVk4/Q6+pD2Tx4sVERETQpk0bAJ599lkWLVp0bZWKiIijIUOsgaLbtytsSJGR68Dx1ltvMWbMGMLDw0lKSgLgjjvuYPny5bz66qtOL1BEpMhLSYGxY60pyi968EEoV851NYk4Wa4Dx6JFi/j555+ZNWsWAQEBANSrV4/FixezdOlSpxcoIlIQxCWl8fuxWLYdOMPvx2KJS0pzzhOfOgWdO8MLL0D//qBebimicn2XipubG2XKlAEcb4P19PQkNTXVeZWJiBQQh84k8O7maA6dTbTvqxrky9DW1agSdB1TikdGWsvKHzwIAQFWK4cGhkoRlesWjpSUFH799dcs+9evX09GRoZTihIRKSjiktKyhA2Ag2cSWbAl+tpbOlasgNatrbARFgZbt0K3btdfsEgBlesWjokTJ9KyZUs6duzIH3/8wbBhw9i7dy8//vgjq1atyosaRURc5si5RA6dTSQtM5Pk1AzSMw0ebjZKeLlz8EwiR84lUtcnIOdPaIw1p8bTT1vbnTrBJ5/AXy3HIkVVrls4unfvzrZt2yhTpgzBwcH88ssv1KpVi59++okuXbrkRY0iIi4Tn5xOUloGUTEJHIhJ4NCZRA7EJBAVk0BSWgbxyem5e8ILF2DBAuv7hx+G1asVNqRYyHULB1iDRBdc/IURESnCSni6c/hsIkmpjl3GSakZHD6bSAlP99w9YalS1twaGzfCAw84sVKRgs2pc5E/oF8eESliSni6EVLGJ9tjIWV8KOGZgz+jO3ZYk3ldVLu2woYUO7lu4Rg+fPhlj61Zs+a6ihERKWhik9Lo2aASqzjKvhMX7PtrVShJzwaViL3aoNHFi61ZQ9PSIDRUi69JsZXrwLF69WpuueUW+3ZGRgZHjx5l165d9OjRw6nFiYi4WklvD77Zd4pWNcrSvf4NpKZn4uXhxvmkNL7Zd4rwkJrZPzAzEyZMgClTrO1bb4Xw8PwrXKSAyXXgGDZsGFOnTs2yf8+ePbz77rtOKUpEpKCoHOhLpdI+7Dp0PsuxqkG+VA70zfqghAS4915YtszaHjMGpk0D91yO9xApQnI9hiO7sAFQu3Zttm/fft0FiYgUJP4+ngxtXY2qQY7B4uLEX/4+no4POHgQ2rSxwoaXF7z7LkyfrrAhxV6uWzgOHTqUZV98fDxbtmzh5MmTTilKRKQgqRLkx8jOtThyLpH45HRKlfCgcqBv1rABsHIl/PwzlC9vhY6/FrkUcba4pLSc/UwWELkOHNWqVXOY0hzAGEPVqlV58803nVaYiEih9OijcP68teJr1aqurkaKqDybbj8P2YzJ3UpBrVq14uOPP/77CWw2SpUqRWBgoNOLyy9xcXEEBAQQGxuLv7+/q8sRkQLmin/cS5eAmTOt21xLlXJdkVJsxCWl8cqX+7JMtw/Wz+XIzrXytaUjp5+huR7DMWfOHKpWrWr/qlKlSp6GjZSUFEaNGkV4eDgRERG0aNGC5cuX248bY5g8eTKNGzemefPmDB48mNjY2DyrR0SKlyutpfLh+l9J63G7NSh08GCt9Cr54uJ0+9m5ON1+QZTrwBF+hdu6+vTpc13FZGfKlCmsWLGCjRs38u233zJnzhwGDBjAzz//DMArr7zC0qVL2bx5M9u3b8fLy4t77rnH6XWISPF0uT/uZU8d5Z7HB+K55n9QogQMGqSVXiVfXG06/VxPt59PcjSGo2PHjjl6sl27dl1PLZd9zmbNmhEQYC2O1KhRIwICAvj666+pX78+06ZN49lnn8XHx5oJcMyYMdSrV49ffvmFm266yen1iEjxkt0f7xv37OSh15+iZEIcqcEV8Pp8FTRt6oLqpDgqVeLKH91XO+4qOaoqKiqKoUOHXvW86Ojo6ywnq759+zJu3DgOHTpElSpVWLt2LTExMQQHBxMZGUlMTAxNL/lFr1OnDn5+fqxfv16BQ0Su2z//eLf7Zjl3v/8iHhkZHAitS/qSpdRqXNtF1UlxVDnQl6pBvhw8k/0YjmznhikAchQ4Bg4cyIQJE656XkpKynUX9E9Dhw4lMTGRBg0acMMNN7Bv3z769etH//79WblyJQDBwcH28202G8HBwURFRV2xzktrjYuLc3rdIlI0XPrH3SfxArevnIdHRgZbW3Tl6zFTeaRODVeXKMXMxblhFmyJdggdl50bpoDIUeC43GRf/1SvXr3rKiY78+bNY9q0aezcuZMaNWrw888/s379etzc3EhMtC60t7e3w2O8vb3tx7Lz/PPPM2nSJKfXKiJFz8U/7vO+i2JPchrPPzCVhnt3sGfow/zr5uoF9o+7FG25mhumgLimjp64uDi2bdvGiRMnuPSu2mnTpnH33Xc7rThjDE8++SSjR4+mRg3r/yLCw8N5/PHHSUpKok6dOkDWlpWUlBR8fS/fpDRu3Dgef/xxh/cTEhLitLpFpAjZuxefHZHUrtGcmuVLklqvAhm9O1PbTQNExbX8fTyp6xPg6jJyLNeBY8uWLfTq1QtPT0/Onz9PcHAwqampHD9+nBtuuMGpxcXExHDu3DmqVavmsD80NJSlS5cyf/58AE6ePEnlypXtx0+ePEn16tUv+7ze3t5ZWkVERLJYuxZz112UTkrmz3FzOVitjsPhP09dyPc5D0QKq1zfFjtu3DiWL1/OsWPHaNGiBVFRURw9epTvvvuOAQMGOLW4smXL4u3tzfHjxx32Hz9+HF9fXxo0aEC5cuXYuXOn/dju3btJSEigc+fOTq1FRIoRY+DVV+HWW7HFxhJdtTZnywRnOa0gz3kgUtDkOnDYbDbatm0L4NCd0rp1a/7880/nVQa4ubkxZMgQ5s2bx7lz5wD48ccf+fLLL+nfvz/u7u6MHTuW2bNnk5SUBMCMGTPo2bMn9evXd2otIlJMpKZas4aOHAmZmZzqN4iXnniDeP8y2Z5eUOc8ECloct2lkpSUhDEGm82GMYaoqChCQ0M5ffo0kZGRTi/wlVdeYeLEiXTq1AlfX1/i4+OZNm0ajz32GACjRo3iwoULtGnTBg8PD2rWrMnChQudXoeIFAOnT0PfvrBxI7i5wfTpnL5rOOlf/nHZhxTUOQ9ECppc/6bUrVuXm2++meXLl9OrVy+aNGlCkyZNiIyMpFOnTk4v0NfXlxdffPGyx202G8888wzPPPOM019bRIqZt9+2woa/P3z0Edx6K5WT0grlnAciBU2OFm+75ZZbWLNmDQDnz5/n6NGj1KpVCw8PD1566SU2bdpE3bp1efrppylVCBcv0uJtIgJARgaMGAEPPQR169p3HzqTcNk5Dwrqypwi+SWnn6E5Chx+fn60b9+egQMH0qdPH/z8itYvmAKHSDFlDCxaBHfdBVe5cy0uKa1QzXkgkl+culps8+bNmT17NkeOHKFTp04MHDiQVatWkZ6uwVIiUkglJ8O998KQIfDvf191pVd/H0/qVgygRfUg6lYMUNgQyaUctXD806+//sqHH37I2rVradq0KYMGDSIiIiIv6ssXauEQKWZOnIDevWHbNnB3h1mzrG4UEck1p3apXMn333/PokWLWLNmDf369bviAM+CSoFDpBj58Ufo1QuOHIHAQPj0U8iDAe8ixYVTu1QuZ+fOnSxZsoQVK1YQHR1tn/lTRKRA+vRTaNvWChs33mi1cChsiOSLHAWObdu22b/fv38/zz77LLVr16Z58+bMmTOHdu3asWLFiiwzgoqIFBjnz8ODD0JSEtxyC2zdCjVruroqkWIjR/NwPPLII9x777188MEH/PDDD7i7u9OlSxfGjx9P7969i9xdKyJSBJUuDYsXw5o18MIL1tgNEck3ORrD4ebmhs1mo02bNgwcOJD+/fsTFBSUH/XlC43hECmijhyB6GirG0VE8kROP0Nz1MJRt25dVq9erSXcRaTw2LoV+vSxulC2bbPGbIiIy+RoDMeLL76osCEihceiRdC+vXX7a5UqV53US0TyXo4Cx6233prXdYiIXL/MTBg71prQKyUFbr8dNm+GatVcXZlIsXddt8WKiBQY8fHWZF4vvGBtjxsHy5dDIVzfSaQo0rrKIlI0zJgBq1ZZ3SfvvAN33+3qikTkEgocIlI0jBsHv/0GY8ZAixaurkZE/sGpXSq33367M59OROTK/vc/a0l5sFo2Pv1UYUOkgMpRC0fHjh1z9GS7du26nlpERHImPd1qyXj1Veu/06e7uiIRuYocBY6oqCiGDh0KQEJCAu+//z6dO3emSpUq2Gw2Dh06xBdffMGQIUPyslYREWuK8rvugnXrrG1/f2tpeZvNpWWJyJXlKHAMHjyYCRMmAPDAAw/w3XffUb16dYdzoqKieOaZZ5xfoYjIRfv2Wbe67t0Lvr6wcCH07evqqkQkB3IUOJ599ln793v27MkSNgBCQ0OJjo52WmEiIg7Wr4c777RaOEJCYOVKaNTI1VWJSA7letDo0aNH2blzZ5b9O3bs4NixY04pSkTEwblzcMcdVtho2RK2b1fYEClkcn1b7NixY2nZsiVt27a1t3QcOHCAzZs3M2fOHKcXKCJCYKA1t8YXX8CcOVCihKsrEpFcytFqsf/0/fff8/rrr7N7924A6tSpw6OPPkrLli2dXmB+0GqxIgXQmTNw9Cg0aODqSkTkCpy6Wuw/tWrVilatWl1zcSIiV/Tbb9bg0KQk+OEHqFTJ1RWJyHW6pom/Dh8+zIQJExg9ejQAy5cv548//nBqYSJSTH3xBbRqBQcOWF0ncXGurkhEnCDXgeO7777jxhtvZPny5axevRqAtLQ0+vTpw1dffeX0AkWkmDDGmsCrZ09rIbaICGtwaJ06rq5MRJwg14Fj/PjxfPXVV0RGRhIcHAxA//792bBhA88995zTCxSRYiAlBYYNgyeftILHAw9YE3uVLevqykTESXIdOIwx9vEbtktm9itXrhwZF9c0EBHJjWefhffeA3d3eO01604ULy9XVyUiTpTrwBEbG0t8fHyW/YcPH+b06dNOKUpEipmxY60ulNWr4ZFHNE25SBGU67tUBg0aRIsWLfjXv/5FTEwMCxcuZM+ePbz33ns88cQTeVGjiBRF27dDs2ZWuChZEjZsUNAQKcKuaR6OuXPnMnXqVA4dOgRAlSpVePrpp7n//vudXmB+0DwcIvnIGHjuORg/HqZNg6eecnVFInId8nQejgceeIAHHniACxcuAFCyZMlrq1JEipekJBg+HD7+2No+eVIrvYoUE7kewzFp0iT79yVLlqRkyZJcuHCBsLAw3nzzTacWJyJFyNGj0K6dFTY8PGDuXHj5ZYUNkWIi14Hj22+/zbKvZMmS7Nmzh0WLFjmlKBEpYi6O19ixA4KCrJVfC2kXrIhcmxx1qURGRrJr1y4ATpw4wcKFC7Occ+7cOc6dO+fU4kSkCDh7Fjp3tibzqlcPVq2C0FBXVyUi+SxHgWPDhg3MnDkTgJMnTzJhwgSH425ubpQrV44pU6Y4vUARKeTKlIEXX7SmLP/gA9DAbJFiKdd3qXTr1o21a9fmVT0uobtURJzswgWIiXFsycjMBLdrWr5JRAqwnH6G5vq3v6iFDRFxsuhoaNMGuna1ulMuUtgQKdauKXDccccdvP766/Z9b775JuPHj9fU5iLF3XffWYNDIyOtMRuHD7u6IhEpIHIdOF588UWaNm3K3Xffbd/Xv39/0tPTGTlypDNrE5HCZP586NgRTp+Ghg2tO1PCw11dlYgUELkOHGlpafznP/8hMDDQvi8oKIjnn3+en3/+2anFiUghkJEBo0fDffdBWhr07Wu1dFSp4urKRKQAyXXguDi7aG6PiUgRNWGCNYHXxe8/+QT8/Fxbk4gUOLkOHDVr1uShhx4iOjravu/gwYM89thjhIWFObM2ESkMRo605tdYvBgmTtTgUBHJVq7XUnnttdfo06cPNWrUwN3dHYCMjAxat27N0qVLnV6giBRAf/4JF/8Ho2xZ2LXLmq5cROQycv0Xonz58mzevJkNGzbw22+/AVC/fn3at2/v7NpEpCCaPRseewzeessatwEKGyJyVdf8V6JDhw506NDBYd+yZcu44447rrsoESmA0tJgxAi4uEjj5s1/Bw4RkavIUeBITU3F09MTm83Gxo0bL3velClTFDhEiqKzZ+HOO+Hrr63VXadOhaeecnVVIlKI5ChwhIWFUbt2bdatW3fFrhOblpkWKXp274aePWH/fihZ0loP5fbbXV2ViBQyOQocy5Yto1SpUgBERESwYcOGbM/7ZxeLiBRyp09Dq1YQGwvVqsFnn8FNN7m6KhEphHIUOJo2bWr/fvbs2Zc970rHRKQQKlsWxoyBdetg6VIoV87VFYlIIZXrG+br1Klz2WOvvPLKdRUjIgVASgqcPPn39tNPw1dfKWyIyHXJUQvH8OHDc/Rka9asua5iRMTFTp2ypiZPSIBNm6wZQ2028PR0dWUiUsjlqIVj9erVGGMwxpCens6SJUv4888/SU1NJS0tjf379/Phhx9qDIdIYRYZCc2bW+ugHDhgDRYVEXGSHLVw9OjRg7fffhuAMWPGsH79epo3b+5wzg8//MC7777r/ApFJO+tWAGDB1stG2FhsGoV1K7t6qpEpAjJUQvHxbABsHPnzixhA6BZs2bs1v8RiRQuxlhzavTpY4WNTp1g2zaFDRFxulwPGo2KiuLgwYPZ7o+KinJKUSKSTyZNsgaFAjzyCKxeDWXKuLYmESmScj21+X333UfDhg3p3bs31atXB+DAgQOsWLGCJ554wqnF1a5dmwoVKjjsO3LkCBUrVrTPePrWW28xd+5cSpQoQenSpZk7dy6VKlVyah0iRdbw4TBvHvz3v/Dgg66uRkSKsFwHjvHjx1OzZk1ee+01Vq5cCVi3ys6ZM4e77rrLqcVVqFCBb775xmFfv3797INTly1bxqRJk4iMjKRs2bJMnjyZHj16sHPnTty0RLYIAKfikjlwOoHYpDRK+3hS3SRQrkYV62CVKrBvH/j6urZIESnybMYY4+oiLicqKorQ0FD79tmzZwkNDSU6OprAwEAaN25Mt27deP755wGIjY2lbNmyLFu2jJ49e+b4deLi4ggICCA2NhZ/f3+nvw8RV9l9LJaN+2KwudlITc+kzsbVtHthLCdfn0vl+wa7ujwRKQJy+hl6Tc0ACQkJvPvuu/aJvr777jvOnTt3bZVewaVhA+Cjjz6ie/fuBAYGcvbsWX766SeHWVADAgKoVasW69evd3otIoXNqbhkfjkWxxe/HmfaF7+TOf4ZOk0agWdyEubjxZyKS3Z1iSJSjOQ6cPz222+EhoYyYsQI3vxrmeqff/6Zli1b8tNPPzm9wEstWLCAYcOGAdgHqAYHBzucU6FChasOXk1JSSEuLs7hS6SoOR6bxPtbo/nzwCneWDGNx7Z8DMDc5nfwaM/RHI9NcnGFIlKc5DpwjB49mlmzZhEXF2cfnPnwww/z+eefM3bsWKcXeNHvv//OiRMn6NKlCwCJiYkAeHt7O5zn7e1tP3Y5zz//PAEBAfavkJCQvClaxIXOJqRy5vc/+fSDJ+m+bwsp7h6MuXUkz3cYzs/HEjibkOrqEkWkGMl14EhOTmbAgAGA43L0NWvWJDU17/6ALViwgHvvvdc+GNT3r0FuKSkpDuelpKTYj13OuHHjiI2NtX8dPnw4b4oWcaHMkzGsWPg49U4d4LRfaYYNnsb/GnfDy8MNmw2S0zJdXaKIFCO5vkslNjaW9PR0PDwcH3r+/HlOXrrgkxNlZGTwwQcf8O2339r3Xbwl95+veWkryOV4e3tnaRkRKWr8Qm5gfYMOhO/fxf13jOdoQHlIy8DDzUaQnxeBfl6uLlFEipFct3B07dqVLl26sGzZMuLj49m4cSNz586lXbt29OnTJy9qZN26ddSoUYOwsDD7vsDAQBo1asTOnTvt++Li4ti3bx+dO3fOkzpECryMDPhrTFLl0j5sefAphgx/xQobfynh6U7z0DJULu3jqipFpBjKdeCYOnUqrVq1YvDgwezcuZP27dszcuRIevbsyeTJk/OiRofBopf673//y3vvvceZM2cAmDVrFvXr1+fWW2/NkzpECrS4OLj9dusrNZX45DRa3liBerVuoHpZP6oF+VK9rB9NqwXSOqws8clprq5YRIqRa56HIzk5mT///BOAsLAwSpQo4dTCLjp//jxhYWFER0dTsmTJLMfnzJljn2k0MDCQt956i8qVK+fqNTQPhxR6+/dbQeP336FECfjmG7aVC2P+5ijq3uCPv48nKWkZeHu6E5eUxu/H4xjeJpQW1YNcXbmIFHI5/QzNdeAIDAykdu3afP/999ddZEGhwCGF2oYN0K8fnD0LN9wAK1dCs2b8fiyWGev2XfZho7vWom7FgHwsVESKojyb+CskJIQtW7ZcV3Ei4iRvvQVdu1pho2lT+OEHaNYMgMqBvlQNyv6OrapBvlQO1HTmIpJ/ch04brzxRjIyMrI99swzz1x3QSKSQ1OnWguupafDgAGwcSNcsnChv48nQ1tXyxI6qgb5MrR1Nfx9PPO7YhEpxnLdpTJ//nyWLFnCoEGDqFSpEu7u7vZjI0eO5Mcff3R6kXlNXSpSKO3eDa1awZNPwrhxcMm8OJeKS0rjyLlE4pPTKVXCg8qBvgobIuI0eTaG40qrsNpstsu2fhRkChxSaCQkgJ/f39unT0PZsq6rR0SKvTwbwxEREUFmZma2X+3atbuuokXkCtatg9BQ+Oabv/cpbIhIIZHrwDFv3rzLHvv444+vqxgRyYYx8Oqr0L07xMTAX6s0i4gUJjme2vzTTz9l6dKleHp6MmzYMDp27JjlnH+u3Coi1yk1FR5+GC4G/WHD4K9VmkVECpMctXC8/fbbDBo0iL179/LTTz/RtWtXvvzyy7yuTaR4i4mBLl2ssOHmBjNmwDvvgNYBEpFCKEctHK+//jrffvstrVu3BmDx4sW88sorV10kTUSu0cmT0LIlREeDvz98/LHVpSIiUkjlqIXD19fXHjYA7rrrLs6dO5dnRYkUe+XLQ5s2UKMGbN2qsCEihV6OAoePT9ZVJbPbd9ttt11/RSLFlTGQkmJ9b7PB22/D9u1Qp45r6xIRcYIcdakcP36cRYsWcemUHSdOnMiyLyoqyvkVihQHyclw//1w4QIsXWqN2fDxsb5ERIqAHE38daXJvhyeTBN/ieTeiRPQuzds2wbu7vDdd9b4DRGRQsCpE39dabIvTfwlch1+/NFabG3bNggMhLVrFTZEpEjKUeB48cUXc/RkOT1PRIAlS6BtWzhyBG680QodnTq5uioRkTyRo8DR7K/lrp11nkixN3Mm3HknJCVBt27WnSg1a7q6KhGRPJPrqc1FxAlatbIm8Bo1Cj7/HEqXdnVFIiJ5KsdTm4vIdUpPB4+/fuVatIDff4fq1V1bk4hIPlELh0h+2LYNateGn376e5/ChogUIwocInnt/fchIgL274f//tfV1YiIuIQCh0heycyEsWPhnnusGURvv91aE0VEpBhS4BDJC/Hx1mReL7xgbY8bB8uXQ6lSLi1LRMRVNGhUxNlOnbLm0/j1V+tOlPnzYdAgV1clIuJSChwizhYUBCEhcPo0rFwJzZu7uiIREZdT4BBxlsxMa9E1d3f46COrW6VyZVdXJSJSIGgMh8j1Sk+HkSPhgQesJeYBAgIUNkRELqEWDpHrcf483HUXrFtnbd9/vzWpl4iIOFALh8i12rfPWtl13Trw9bUWY1PYEBHJllo4RK7Fl19C//5WC0dIiDU4tFEjV1clIlJgqYVDJLfefhu6d7fCRsuWsH27woaIyFUocIjkVtWq1uDQe++FDRugQgVXVyQiUuCpS0UkJ4wBm836vmtXq1WjceO/94mIyBWphUPkan77DZo1gz/++HtfkyYKGyIiuaDAIXIln38OrVrBzp3WXBsiInJNFDhEsmMMTJ9urfAaH28tL//ee66uSkSk0FLgEPmnlBQYNgyefNIKHg88YM21UbasqysTESm0NGhU5FJnzkDPnvD999aaKDNnwsMPa7yGiMh1UuAQuZSfn/Xf0qXhk0+gSxeXliMiUlQocIhcqkQJWLYM4uKgVi1XVyMiUmQocEjxZgxMmQJJSTB1qrWvQgVN5iUi4mQKHFJ8JSbC8OGweLG13aePNd+GiIg4nQKHFE9Hj0KvXtb8Gh4eMHu2woaISB5S4JDiZ/t26N0bjh+HoCBYutSaZ0NERPKM5uGQ4uXjj6FdOyts1KsHP/ygsCEikg8UOKR4sdmsib169oQtWyA01NUViYgUC+pSkeLlrrugTBno2NGa2EtERPKFWjikaDt4EG69FY4d+3tfly4KGyIi+UyBQ4quzZutO09Wr4Z//9vV1YiIFGsKHFI0vfsudOgAMTHQqJF126uIiLiMAocULRkZMHq0NaFXWhr07QubNkFIiKsrExEp1hQ4pOiIi7PuPnn5ZWt7wgRrAbaLC7KJiIjL6C4VKTpsNjh8GHx8YMEC6N/f1RWJiMhfFDik6ChVCj77DM6ehSZNXF2NiIhcQoFDCrfZs62JvEaNsrZDQzWZl4hIAaTAIYVTWhqMGAFvvml1pbRrp1YNEZECTIFDCp+zZ+HOO+Hrr62wMXUqNG7s6qpEROQKCsVdKgcOHKBv37506NCBevXq0bJlS3bs2AGAMYbJkyfTuHFjmjdvzuDBg4mNjXVxxZJndu+G5s2tsOHnB8uXw9ixVvAQEZECq8AHjpiYGDp16sSIESPYsGEDP//8M76+vvz5558AvPLKKyxdupTNmzezfft2vLy8uOeee1xcteSJ1auhZUvYvx+qVrUWX+vVy9VViYhIDhT4LpUXXniBVq1a0a5dOwA8PDyYO3cuvr6+ZGRkMG3aNJ599ll8fHwAGDNmDPXq1eOXX37hpptucmXp4mwHDlhzbbRtC8uWQblyrq5IRERyqMC3cCxbtsweNi4KCwujYsWKREZGEhMTQ9OmTe3H6tSpg5+fH+vXr8/vUiWvPfQQvP8+fPWVwoaISCFToANHQkICUVFRZGRkcPfdd9OmTRu6devG6tWrAWtsB0BwcLD9MTabjeDgYKKioi77vCkpKcTFxTl8SQF06hQMGQLnzlnbNhvcfTd4ebm2LhERybUC3aVy/vx5AMaPH8+GDRsIDw/nq6++soeOxMREALy9vR0e5+3tbT+Wneeff55JkyblWd3iBJGRZPTogfvhw5w4cZaDby4gtKwf5f1LuLoyERG5BgW6hcPd3R2Anj17Eh4eDkCnTp3o2LEjr776Kr6+voDVYnGplJQU+7HsjBs3jtjYWPvX4cOH8+gdyDVZsYLMVq1xP3yYQ2Uq8vCNvXn4gx8ZuyyS34/pDiQRkcKoQAeOcuXK4e3tTaVKlRz2V61alaioKKpXrw7AyZMnHY6fPHnSfiw73t7e+Pv7O3xJAWCMNadGnz64JSawrXojhj4wi4PlqgAQeTiWl9bt5VRcsosLFRGR3CrQgcPd3Z02bdpw/Phxh/0nT56kSpUqNGjQgHLlyrFz5077sd27d5OQkEDnzp3zu1y5HklJ1viMp58GYHGLXjx2z3PE+TqGwcjDsRw4neCKCkVE5DoU6MAB8NRTT7Fy5UoOHToEwO+//866det4+OGHcXd3Z+zYscyePZukpCQAZsyYQc+ePalfv74ry5bcunDBmlfDw4PfnnmB6bc9TIZ79kOMYpPS8rk4ERG5XgV60ChA165dmTVrFr169aJkyZKkp6fz3nvv0aNHDwBGjRrFhQsXaNOmDR4eHtSsWZOFCxe6uGrJtXLlrJVez5whvmoD+ODHy54a4OOZj4WJiIgz2IwxxtVFuFpcXBwBAQHExsZqPEd+WrwYUlPhHzPDnopLZuyySCIPZx0g2iAkgGl3NNDdKiIiBUROP0MLfJeKFEGZmfDMMzBgAPzrXxAZ6XC4vH8JxnS9kQYhAQ77G4QEMKbrjQobIiKFUIHvUpEiJiEB7r3XmpocrCXm69XLclrdilZLxoHTCcQmpRHg40l1zcMhIlJoKXBI/jl0yFpsbdcua7bQuXOtmUQvo7x/CQUMEZEiQoFD8seWLdCnjzVdefny1rLyrVu7uioREcknChySP776ygob4eHW3ShVqri6IhERyUcKHJI/nn4aSpWyBomWLOnqakREJJ/pLhXJG3FxMGYMXFxEz80NRo5U2BARKabUwiHOd+AA9OwJv/9udaNoIjYRkWJPgUOc65tvoF8/OHMGbrgBHn30mp8qLimNI+cSiU9Op1QJDyoH+uKvWUZFRAolBQ5xnrlz4eGHIT0dmjaFFSvgHyv95tShMwm8uzmaQ2cT7fuqBvkytHU1qgT5OalgERHJLxrDIdcvPR0eewz+/W/r+wEDYOPGaw4bcUlpWcIGwMEziSzYEk2cFm8TESl0FDjk+p08CR9/bH0/ZQp8+CH4+Fzz0x05l5glbFx08EwiR85lf0xERAoudanI9atUyZqqPCbGmtzrOsUnp1/XcRERKXgUOOTarFtnrfTao4e13bat0566VIkr/1he7biIiBQ86lKR3DEGXn0VuneHgQNhzx6nv0TlQF+qBvlme6xqkC+VA7M/JiIiBZcCh+Rcaio88IA1gVdmpnX7a2io01/G38eToa2rZQkdF+9S0a2xIiKFj9qmJWdiYqBvX9i0yZo19MUX4fHHwWbLk5erEuTHyM61NA+HiEgRocAhV/fLL3D77RAdDf7+8NFHcOutef6y/j6e1PUJyPPXERGRvKfAIVf33ntW2KhRw1rptW5dV1ckIiKFjAKHXN20aeDlBaNHQ1CQq6sREZFCSINGJavkZGuMRtpfM3p6eMDUqQobIiJyzdTCIY5OnIDevWHbNjh8GF57zdUViYhIEaDAIX/78Ufo1QuOHIHAQKfMGioiIgLqUpGLPv3Umi30yBGoXRu2b4eOHV1dlYiIFBEKHMVdZiZMmgT9+0NSEtxyC2zdCmFhrq5MRESKEAWO4u7QIZgxw/r+8cfh888hQHNfiIiIc2kMR3FXrZq1nPzJk3Dffa6uRkREiigFjuJo61ZIT/97hdeLK76KiIjkEXWpFDeLFkH79tYdKNHRrq5GRESKCQWO4iIzE8aOhXvvhZQUaNMGypZ1dVUiIlJMKHAUB/Hx1mReL7xgbf/nP7BsGZQs6dKyRESk+NAYjqIuKspa6fXXX8HbG+bPh0GDXF2ViIgUMwocRd2LL1pho0IFWLkSmjd3dUUiIlIMKXAUdS+/bC3CNnEiVK7s6mpERKSY0hiOoiY9Hd591xokCuDjA/PmKWyIiIhLKXAUJefPw223wfDhVouGiIhIAaEulaJi3z5rcOjeveDrC+Hhrq5IRETEToGjKPjyS2vxtfPnISTEGhzaqJGrqxIREbFTl0phZgy8/jp0726FjZYtrWXlFTZERKSAUeAozKKiYPRoyMiwZhDdsMG6/VVERKSAUZdKYVa9unUHyokTMGYM2GyurkhERCRbChyFzW+/WS0aDRpY2/fc49p6REREckBdKoXJF19Aq1bWcvInT7q6GhERkRxT4CgMjIHp06FnT2shturVwd3d1VWJiIjkmLpU8kBcUhpHziUSn5xOqRIeVA70xd/H89qeLCUF/v1veO89a/v++607U7y8nFewiIhIHlPgcLJDZxJ4d3M0h84m2vdVDfJlaOtqVAnyy92TnTgBd9wB338Pbm4wcyY88ogGh4qISKGjLhUniktKyxI2AA6eSWTBlmjiktJy94T/+Y8VNkqXhjVr4NFHFTZERKRQUuBwoiPnErOEjYsOnknkyLnsj13Wyy9b4za2bYMuXZxQoYiIiGuoS8WJ4pPT8XC3Ub9SAKV9PElNz8Tb041ziWn8ejSW+OT0Kz+BMdadKLfdZrVklC4Nn32WL7WLiIjkJQUOJ/Iv4UH7WuVZFXmUfScu2PfXqlCSng0q4V/iCpc7MdFa5XXxYpg1y+o+ERERKSLUpeJEQSW9+XL3CYewAbDvxAW+3H2CoJLe2T/w6FFo184KGx4e4OOTD9WKiIjkHwUOJzp9IYXktEx8vBznyPDxcic5LZPTF1KyPmj7dmjWDHbuhKAgWL8e/vWvfKpYREQkf6hLxYnik9Px8XQntJwfyakZpGcaPNxslPByx9PNLesYjo8+srpRkpOhXj1YtQpCQ11TvIiISB5S4HCiUn+N0fB0c8OzRNbGo1KXjuH4808YPBgyM62pyj/4APz986tUERGRfKXA4USVA32pGuTLwTNZb3+tGuRL5UDfv3eEhVnTlZ86Bc89p6nKRUSkSLMZY4yri3C1uLg4AgICiI2Nxf86WxkOnUlg3ndR7DkeZ+9SqX2DP/9qG0qVC6etlV6rV3dS5SIiIq6V08/QAt/CMXHiRFasWEHp0qXt+8qUKcOyZcsAMMbw7LPPsmLFCjw8PKhVqxZvvPEGAQEBLqoYageXomb5kqSmZ+Ll4Ya7mw3vrVtg2N1Qtixs3aruExERKVYKfOAAmDlzJu3bt8/22CuvvMLSpUvZunUrPj4+DB8+nHvuuYfPXDBh1uWmNm+78TPKLpoG6elQuTJcuKDAISIixUqhvi02IyODadOm8dBDD+Hz19wVY8aMYdWqVfzyyy/5Xs8/pza3ZWbQ/+OZDHt3Cu7p6cTdejts2gQVK+Z7bSIiIq5UqANHZGQkMTExNG3a1L6vTp06+Pn5sX79+nyv59LbXn0SLzBi5uN0W/shACt7/Yvdr84Dv1yuGCsiIlIEFIrAMX/+fNq3b0+bNm0YMmQI+/fvB+DAgQMABAcH28+12WwEBwcTFRV12edLSUkhLi7O4csZLr3tddAH07npl+9J8fLmzYem8lnvByjl6+WU1xERESlsCnzgqFKlCo0aNWL9+vVs2rSJ0NBQmjRpwtGjR0lMtLovvL0dpwz39va2H8vO888/T0BAgP0rJCTEKbVevC0W4NP+j/JnjZuYNu5tdjTrnPW2WBERkWKk0N0Wm5GRQaVKlbjvvvto3Lgx/fr14/Dhw1SuXNl+To0aNbjtttuYNWtWts+RkpJCSsrf04zHxcUREhLitNtiF2yJtubiMAZsNqoG+TK0dTWqBKk7RUREipYic1vsP7m7u1OtWjX2799Pv379ADh58qRD4Dh58iTVrzDXhbe3d5ZWEWepEuTHyM61OHIukfjkdEqV8KByoC/+Pp558noiIiKFQYHvUhkxYkSWfceOHaNKlSo0aNCAcuXKsXPnTvux3bt3k5CQQOfOnfOzTAf+Pp7UrRhAi+pB1K0YoLAhIiLFXoEPHJ999pnDnBrz5s0jJiaG4cOH4+7uztixY5k9ezZJSUkAzJgxg549e1K/fn1XlSwiIiL/UOC7VJ577jlmzpzJyy+/TGpqKt7e3qxfv57atWsDMGrUKC5cuECbNm3w8PCgZs2aLFy40MVVi4iIyKUK3aDRvODMtVRERESKk5x+hhb4LhUREREp/BQ4REREJM8pcIiIiEieU+AQERGRPKfAISIiInlOgUNERETynAKHiIiI5DkFDhEREclzChwiIiKS5xQ4REREJM8V+LVU8sPF2d3j4uJcXImIiEjhcvGz82orpShwAPHx8QCEhIS4uBIREZHCKT4+noCAgMse1+JtQGZmJseOHaNUqVLYbDanPGdcXBwhISEcPnxYC8JdB11H59B1dA5dR+fRtXSOgnAdjTHEx8dTsWJF3NwuP1JDLRyAm5sblStXzpPn9vf31y+TE+g6Ooeuo3PoOjqPrqVzuPo6Xqll4yINGhUREZE8p8AhIiIieU6BI494e3szYcIEvL29XV1Koabr6By6js6h6+g8upbOUZiuowaNioiISJ5TC4eIiIjkOQUOERERyXMKHCIiIpLnFDiuw8SJE2nYsCHt27e3f91xxx3248YYJk+eTOPGjWnevDmDBw8mNjbWhRUXbAcOHKBv37506NCBevXq0bJlS3bs2AHoWuZU7dq1HX4e27dvT1hYGO3atbOf89Zbb9GkSRPatGnDbbfdxtGjR11YccGVkpLCqFGjCA8PJyIighYtWrB8+XL7cf1M5kxqaiqTJk2iZcuWtGrVirZt29p/r0HX8XJSU1MZO3YsHh4eREdHZzl+td/j1NRURowYQdOmTWnSpAmPPfYYqamp+VT9ZRi5ZhMmTDAbNmy47PEZM2aYBg0amMTERGOMMcOGDTM9e/bMp+oKl1OnTplq1aqZb7/91hhjTFpamunQoYP56KOPjDG6ljkVERGRZV/fvn3N66+/bowxZunSpeaGG24wMTExxhhjJk2aZBo2bGgyMjLys8xC4b///a+pVq2aOX/+vDHGmB9//NF4eXmZXbt2GWP0M5lTjz/+uKlfv76Ji4szxhjz0UcfmcDAQHPy5EljjK5jdqKiokzLli3NvffeawATFRXlcDwnv8ePPvqo6datm0lPTzfp6emmc+fO5tFHH83Pt5GFAsd1uFLgSE9PN+XKlTNz5syx7/vtt98MYCIjI/OpwsJj9OjRZuDAgQ77/vjjD3P06FFdy1w4cOCAw/aZM2eMv7+/OXv2rDHGmEaNGpmxY8faj58/f954eHiYzz77LF/rLAx69Ohh7rzzTod95cqVMy+//LJ+JnMoIyPD+Pr6mpdfftlhf4UKFczkyZN1HS/jl19+MX/88YfZsGFDtoHjar/Hp0+fNp6enmbNmjX2c7744gvj6elpzpw5ky/vITvqUskjkZGRxMTE0LRpU/u+OnXq4Ofnx/r1611YWcG0bNkyh2Z/gLCwMCpWrKhrmQuhoaEO2x999BHdu3cnMDCQs2fP8tNPPzlcx4CAAGrVqqXrmI2+ffuyadMmDh06BMDatWuJiYkhODhYP5M5dPr0aRITEwkODnbYX6FCBTZu3KjreBn169cnLCws22M5+T3euHEjaWlpDuc0a9aMtLQ0vv3227wt/gq0lsp1mj9/PhMnTiQtLY2wsDCeeeYZatSowYEDBwAcftFsNhvBwcFERUW5qtwCKSEhgaioKDIyMrj77ruJjo6mZMmSjBw5ku7du+taXocFCxYwZcoUAPu1yu6Pv65jVkOHDiUxMZEGDRpwww03sG/fPvr160f//v1ZuXIloJ/JqylXrhx+fn720AbWmI3jx4+TnJys3+1rkJPf4wMHDuDh4UFQUJD9eLly5XB3d3fpdVULx3WoUqUKjRo1Yv369WzatInQ0FCaNGnC0aNHSUxMBMgy+5u3t7f9mFjOnz8PwPjx43nyySfZvHkzTz75JD179uTLL7/UtbxGv//+OydOnKBLly4Auo65NG/ePKZNm8bOnTvZvXs3P/74Iy1btsTNzU3XModsNhuPPPIIc+fOtQ9qfPPNNzl79iwZGRm6jtcgJ9csMTERLy+vLI/18vJy6XVVC8d1GD58uMP2+PHjmTNnDrNnz6Zx48aANdL9UikpKfj6+uZbjYWBu7s7AD179iQ8PByATp060bFjR1599VWGDRsG6Frm1oIFC7j33nvty0VfvFbZXUc/P798r68gM8bw5JNPMnr0aGrUqAFAeHg4jz/+OElJSdSpUwfQz2ROPPfccwQFBXHXXXdhs9mIiIhg4MCB7Nmz54o/k7qO2cvJ77Gvr2+2d6Skpqa69LqqhcOJ3N3dqVatGvv376d69eoAnDx50uGckydP2o+JpVy5cnh7e1OpUiWH/VWrViUqKkrX8hpkZGTwwQcf2MMacNnreOLECV3Hf4iJieHcuXNUq1bNYX9oaChLly7Vz2QuuLu788QTT/Ddd9+xadMmpkyZwpkzZ7jpppt0Ha9BTn6Pq1evTnp6OmfOnLEfj4mJISMjw6XXVYHjOowYMSLLvmPHjlGlShUaNGhAuXLl2Llzp/3Y7t27SUhIoHPnzvlZZoHn7u5OmzZtOH78uMP+kydP6lpeo3Xr1lGjRg2HgWeBgYE0atTI4TrGxcWxb98+Xcd/KFu2LN7e3ll+Jo8fP46vr69+JnMhMjKSs2fP2rfT0tLYunUr/fr103W8Bjn5PW7Xrh2enp4O5+zYsQNPT88sg/PzlcvujykCqlWrZlauXGnffvvtt02JEiXM7t27jTHW/eXh4eH2+8vvu+++Yn9/+eWsXbvWBAYGmoMHDxpjrFvjvL29zapVq4wxupa51b9/fzN//vws+5cuXWoqVqxoTp8+bYwx5tlnn9U8HJfxwAMPmBtvvNF+S/HOnTuNp6enmTlzpjFGP5M59fDDD5tJkybZt5955hlzyy232Ld1HS/vcrfF5uT3+NFHHzXdu3c3GRkZJiMjw3Tt2tXl83BoDMd1eO6555g5cyYvv/wyqampeHt7s379emrXrg3AqFGjuHDhAm3atMHDw4OaNWuycOFCF1ddMHXt2pVZs2bRq1cvSpYsSXp6Ou+99x49evQAdC1z4/z583z11Ve88847WY7dcccdnDp1ii5dulCiRAkCAwNZtWqVfZyH/O2VV15h4sSJdOrUCV9fX+Lj45k2bRqPPfYYoJ/JnGrRogVTp07liy++wNPTkyZNmrBkyRL7cV3HrFJTU+natat9QP2AAQMICQnh008/BXL2ezx9+nSeeOIJmjVrBkDr1q2ZPn16vr+XS2l5ehEREclz+t8aERERyXMKHCIiIpLnFDhEREQkzylwiIiISJ5T4BAREZE8p8AhIiIieU6BQ0RERPKcAoeIiIjkOQUOkWKqTZs2dOvWLdePW7FiBStWrHB6PcOGDaNChQoMHTo02+PvvPMONWrUwGaz0aZNG6KiouzHTpw4QUREBCVLluTee++9rtcRkbyhmUZFiqHo6Ghq1qyJMYZjx45Rvnz5HD/24gf1ggULnF7X1Z5706ZNtGvXjhUrVtCrVy+HY9988w0ff/wxc+bMue7XERHnUwuHSDH00Ucf8cQTT5CRkcHixYtdXU6OtW3blpCQED744IMsxz766CMGDRrkgqpEJCcUOESKoSVLljBmzBhatWrFhx9+6HAsPT2dsWPHctNNNxEREUGzZs2YOXMmAE8++SRr1qxhzZo1tG/fnl69erFr1y5atmyJzWYjOjoagHHjxmXptoiOjubOO++kVatWRERE0KVLF37//fdc1W2z2RgwYACrVq0iPj7evj8tLY3t27dz88035/p11qxZQ8OGDbHZbPZ9w4YNo3Tp0kycONHh3IULF9K4cWPatWtHmzZtWL58uf3YmTNn6NevH61bt6Z9+/bcdtttbNu2LVfvT6Qo02qxIsXMr7/+SsWKFSlTpgwDBw7kscceIyoqitDQUACeeeYZvvzyS7Zu3Yqfnx/fffcdt99+OyNHjuTFF1/k1KlTgGN3xMcff2x/PMDzzz/P8ePHs7yuzWZjy5Yt2Gw2Fi1aRJ8+ffjtt9/w8Mj5n6JBgwYxffp0li1bxpAhQwArNHTt2hWbzZbr17nlllsoUaIEHTp0sO979913HcaIAKxdu5bHH3+cXbt2UblyZfbv3094eDhffvklrVq1Yvz48fj6+rJlyxb7dVy9ejUtWrTI8XsTKcrUwiFSzHz44Yf2rof+/fvj7u5ub+VISkrilVde4aGHHsLPzw+wujEuLsl+PSIiIpgzZ469JaF///7s27eP/fv35+p5GjZsSJ06dRy6VS59T856nX+aOnUqAwYMoHLlygDUqFGDDh06MHv2bACOHj3KiRMnSE5OBmDEiBEMHjz4ul5TpChR4BApZlatWmUfcBkcHEz79u3tgePPP/8kOTmZsLAwh8f8s2vhWnh4ePDqq69y8803ExERYb9D5sSJE7l+rkGDBvH1119z4sQJEhIS7K0Nzn6dS/3666+sXr2a9u3b27+ioqJISkoCYOzYsfz8889UrVqVRx99lIMHD2a5jiLFmbpURIqRLVu2EBMTw6233mrfd+LECfbu3cuuXbtwd3e/pue9dPzDRRkZGQ7PN2bMGFavXs3WrVvtd8XYbDau5Ua5gQMHMn78eBYvXky5cuUc7li5lte5XP3/NHjwYCZNmpTtc7Rq1Yro6GiWLVvG/PnzadKkCa+99hqPPPJIbt+eSJGkFg6RYuSjjz5i4cKFfPPNN/avbdu2UaJECT788EPCwsIoUaIEBw4ccHjcSy+9RGJiIgBubn//2UhMTCQjI4NSpUoBOAzkPHr0qMNzbNy4kQ4dOthDQGpq6jW/jxo1atC8eXM++OCDLHenXMvr5KT++vXrs3fvXod9GzZs4M033wRg+fLleHl5cffdd/PVV18xZswY3nrrrWt7gyJFkAKHSDGRkZHBxo0b6dSpk8P+gIAAevbsyccff0yJEiUYNWoUb775pj1grFmzhuXLl+Pr6wtAuXLlOHfuHAD9+vVjz549lClThipVqtgHTO7Zs4ddu3Y5vE7dunX5/vvv7c+7dOnS63o/gwYN4ocffuD8+fMOA1av5XVq1qyJn5+fvf6vvvrKPjj2oqeffprPPvuMn3/+GYCEhAT+85//ULt2bQBeffVV1q1bZz8/LS2NWrVqXdd7FClSjIgUeefPnzfNmzc3QUFB5pFHHnE4Nm/ePFOjRg0DmJYtW5qDBw+aJ5980tSrV8+0a9fO9OzZ0xw6dMh+/u7du039+vVN27ZtzT333GPf/7///c/ceOONpl27dmbMmDFm8ODBJjg42Nx3333GGGOOHDliunfvbqpXr25uv/12M2HCBAOY8PBws27dOjN06FATHBxsgoODzQMPPHDV93T8+HHj7u5uZs2a5bD/Wl9n/vz5JiwszHTs2NE8//zzJiIiwlStWtU8/fTT9nMWLVpkbrrpJtOqVSvTpk0b8/7779uPvf/++6Zly5YmIiLCtG7d2txxxx3m2LFjOfwXEin6NNOoiIiI5Dl1qYiIiEieU+AQERGRPKfAISIiInlOgUNERETynAKHiIiI5DkFDhEREclzChwiIiKS5xQ4REREJM8pcIiIiEieU+AQERGRPKfAISIiInnu/wFC0F3IAlH5EQAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import numpy as np\n",
+        "\n",
+        "# Prediksi hasil dengan model yang sudah dilatih\n",
+        "y_pred = model.predict(X_test)\n",
+        "y_pred_inv = inverse_transform_y(y_pred)\n",
+        "y_test_inv = inverse_transform_y(y_test)\n",
+        "# Plot scatter Actual vs Predicted\n",
+        "plt.figure(figsize=(6, 5))\n",
+        "sns.scatterplot(x=y_test_inv.flatten(), y=y_pred_inv.flatten(), alpha=0.7)\n",
+        "\n",
+        "# Garis referensi y = x (ideal jika prediksi sempurna)\n",
+        "min_val = min(y_test_inv.min(), y_pred_inv.min())\n",
+        "max_val = max(y_test_inv.max(), y_pred_inv.max())\n",
+        "plt.plot([min_val, max_val], [min_val, max_val], linestyle=\"--\", color=\"red\")\n",
+        "\n",
+        "# Label dan judul\n",
+        "plt.xlabel(\"Actual Values\")\n",
+        "plt.ylabel(\"Predicted Values\")\n",
+        "plt.title(\"Actual vs Predicted Values\")\n",
+        "\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ly8uYZlOzavT",
+        "outputId": "01c42102-f238-4779-cb6e-e108e0de19bb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
+            "R-squared: 0.9270\n",
+            "MSE: 0.0219\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# Hitung R-squared (R²)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "print(f'R-squared: {r2:.4f}')\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "print(f'MSE: {mse:.4f}')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jnZVzHxQjDRZ",
+        "outputId": "29c45fe3-a05a-4d19-afe6-eedcf22e6737"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_preds = []\n",
+        "for i in range(120):\n",
+        "    X_pred = df.iloc[i, :-1].values  # Ambil fitur dari setiap baris\n",
+        "    X_pred = scaler_X.transform(X_pred.reshape(1, -1))  # Normalisasi\n",
+        "    X_pred = X_pred.reshape(1, 18, 1)  # Ubah ke bentuk yang sesuai untuk model\n",
+        "\n",
+        "    y_pred = model.predict(X_pred)  # Prediksi dengan model terbaik\n",
+        "    y_pred = inverse_transform_y(y_pred)  # Invers transformasi jika diperlukan\n",
+        "\n",
+        "    # Pastikan bentuk y_pred adalah (1, 1) sebelum mengakses indeks\n",
+        "    y_preds.append(y_pred.item())  # Gunakan .item() untuk mengambil nilai skalar\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n6d3HV-zl9PN",
+        "outputId": "4c28a0d7-d0d7-49a7-a1b2-4270aab379ce"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data 1: Prediksi = 99.68636322021484\n",
+            "Data 2: Prediksi = 99.68636322021484\n",
+            "Data 3: Prediksi = 99.68636322021484\n",
+            "Data 4: Prediksi = 99.68636322021484\n",
+            "Data 5: Prediksi = 99.68636322021484\n",
+            "Data 6: Prediksi = 99.68636322021484\n",
+            "Data 7: Prediksi = 99.68636322021484\n",
+            "Data 8: Prediksi = 100.78865814208984\n",
+            "Data 9: Prediksi = 100.78865814208984\n",
+            "Data 10: Prediksi = 100.78865814208984\n",
+            "Data 11: Prediksi = 100.78865814208984\n",
+            "Data 12: Prediksi = 82.61831665039062\n",
+            "Data 13: Prediksi = 102.59558868408203\n",
+            "Data 14: Prediksi = 102.59558868408203\n",
+            "Data 15: Prediksi = 102.59558868408203\n",
+            "Data 16: Prediksi = 99.21025085449219\n",
+            "Data 17: Prediksi = 100.78865814208984\n",
+            "Data 18: Prediksi = 101.39962768554688\n",
+            "Data 19: Prediksi = 101.39962768554688\n",
+            "Data 20: Prediksi = 101.39962768554688\n",
+            "Data 21: Prediksi = 88.90672302246094\n",
+            "Data 22: Prediksi = 88.90672302246094\n",
+            "Data 23: Prediksi = 91.22688293457031\n",
+            "Data 24: Prediksi = 91.22688293457031\n",
+            "Data 25: Prediksi = 88.5420150756836\n",
+            "Data 26: Prediksi = 86.48829650878906\n",
+            "Data 27: Prediksi = 91.18412017822266\n",
+            "Data 28: Prediksi = 92.11151123046875\n",
+            "Data 29: Prediksi = 90.83267974853516\n",
+            "Data 30: Prediksi = 91.18412017822266\n",
+            "Data 31: Prediksi = 90.83267974853516\n",
+            "Data 32: Prediksi = 90.50383758544922\n",
+            "Data 33: Prediksi = 91.18412017822266\n",
+            "Data 34: Prediksi = 91.22688293457031\n",
+            "Data 35: Prediksi = 91.18412017822266\n",
+            "Data 36: Prediksi = 90.50383758544922\n",
+            "Data 37: Prediksi = 91.18412017822266\n",
+            "Data 38: Prediksi = 91.18412017822266\n",
+            "Data 39: Prediksi = 91.18412017822266\n",
+            "Data 40: Prediksi = 91.18412017822266\n",
+            "Data 41: Prediksi = 87.46307373046875\n",
+            "Data 42: Prediksi = 80.15849304199219\n",
+            "Data 43: Prediksi = 80.15849304199219\n",
+            "Data 44: Prediksi = 81.3090591430664\n",
+            "Data 45: Prediksi = 78.29226684570312\n",
+            "Data 46: Prediksi = 80.15849304199219\n",
+            "Data 47: Prediksi = 80.25145721435547\n",
+            "Data 48: Prediksi = 82.44479370117188\n",
+            "Data 49: Prediksi = 81.39000701904297\n",
+            "Data 50: Prediksi = 81.39000701904297\n",
+            "Data 51: Prediksi = 80.6285400390625\n",
+            "Data 52: Prediksi = 80.6285400390625\n",
+            "Data 53: Prediksi = 81.60282135009766\n",
+            "Data 54: Prediksi = 81.69881439208984\n",
+            "Data 55: Prediksi = 81.39000701904297\n",
+            "Data 56: Prediksi = 82.86495971679688\n",
+            "Data 57: Prediksi = 80.05894470214844\n",
+            "Data 58: Prediksi = 81.16175842285156\n",
+            "Data 59: Prediksi = 80.3492660522461\n",
+            "Data 60: Prediksi = 79.00870513916016\n",
+            "Data 61: Prediksi = 71.9522933959961\n",
+            "Data 62: Prediksi = 72.0740966796875\n",
+            "Data 63: Prediksi = 65.2937240600586\n",
+            "Data 64: Prediksi = 71.48233795166016\n",
+            "Data 65: Prediksi = 70.94963836669922\n",
+            "Data 66: Prediksi = 70.94963836669922\n",
+            "Data 67: Prediksi = 71.9522933959961\n",
+            "Data 68: Prediksi = 70.94963836669922\n",
+            "Data 69: Prediksi = 70.99336242675781\n",
+            "Data 70: Prediksi = 70.67395782470703\n",
+            "Data 71: Prediksi = 70.97341918945312\n",
+            "Data 72: Prediksi = 70.65409851074219\n",
+            "Data 73: Prediksi = 70.65409851074219\n",
+            "Data 74: Prediksi = 70.65409851074219\n",
+            "Data 75: Prediksi = 69.88272094726562\n",
+            "Data 76: Prediksi = 71.953369140625\n",
+            "Data 77: Prediksi = 72.05660247802734\n",
+            "Data 78: Prediksi = 71.953369140625\n",
+            "Data 79: Prediksi = 73.72779083251953\n",
+            "Data 80: Prediksi = 71.953369140625\n",
+            "Data 81: Prediksi = 60.43072509765625\n",
+            "Data 82: Prediksi = 59.96089172363281\n",
+            "Data 83: Prediksi = 59.96089172363281\n",
+            "Data 84: Prediksi = 59.96089172363281\n",
+            "Data 85: Prediksi = 61.578956604003906\n",
+            "Data 86: Prediksi = 59.894081115722656\n",
+            "Data 87: Prediksi = 59.894081115722656\n",
+            "Data 88: Prediksi = 63.262779235839844\n",
+            "Data 89: Prediksi = 57.74375915527344\n",
+            "Data 90: Prediksi = 59.894081115722656\n",
+            "Data 91: Prediksi = 59.894081115722656\n",
+            "Data 92: Prediksi = 59.894081115722656\n",
+            "Data 93: Prediksi = 59.894081115722656\n",
+            "Data 94: Prediksi = 59.894081115722656\n",
+            "Data 95: Prediksi = 60.98666000366211\n",
+            "Data 96: Prediksi = 61.57034683227539\n",
+            "Data 97: Prediksi = 61.57034683227539\n",
+            "Data 98: Prediksi = 61.57034683227539\n",
+            "Data 99: Prediksi = 60.83067321777344\n",
+            "Data 100: Prediksi = 60.83067321777344\n",
+            "Data 101: Prediksi = 50.637001037597656\n",
+            "Data 102: Prediksi = 50.637001037597656\n",
+            "Data 103: Prediksi = 50.637001037597656\n",
+            "Data 104: Prediksi = 51.16365432739258\n",
+            "Data 105: Prediksi = 52.42210388183594\n",
+            "Data 106: Prediksi = 51.13056564331055\n",
+            "Data 107: Prediksi = 51.13056564331055\n",
+            "Data 108: Prediksi = 54.765586853027344\n",
+            "Data 109: Prediksi = 51.26362609863281\n",
+            "Data 110: Prediksi = 51.13056564331055\n",
+            "Data 111: Prediksi = 51.62769317626953\n",
+            "Data 112: Prediksi = 51.62769317626953\n",
+            "Data 113: Prediksi = 51.13056564331055\n",
+            "Data 114: Prediksi = 51.13056564331055\n",
+            "Data 115: Prediksi = 51.167015075683594\n",
+            "Data 116: Prediksi = 51.167015075683594\n",
+            "Data 117: Prediksi = 51.18085479736328\n",
+            "Data 118: Prediksi = 50.637001037597656\n",
+            "Data 119: Prediksi = 50.637001037597656\n",
+            "Data 120: Prediksi = 50.637001037597656\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i, pred in enumerate(y_preds):\n",
+        "    print(f\"Data {i+1}: Prediksi = {pred}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uy4Non0Emi_K"
+      },
+      "source": [
+        "# Save Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UTQejRHFmN9H",
+        "outputId": "7a4d5c48-42c8-4f32-b4c4-fb2dc0574f69"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        }
+      ],
+      "source": [
+        "model.save('model_new.h5')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n0e8d_2Smvt7"
+      },
+      "source": [
+        "# Save Tensorflow Lite Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SXkqcQ2amyZ_",
+        "outputId": "05ed8a34-bdfc-48a7-bb82-6dfc74ed4e20"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saved artifact at '/tmp/tmpn7uirqnd'. The following endpoints are available:\n",
+            "\n",
+            "* Endpoint 'serve'\n",
+            "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 18, 1), dtype=tf.float32, name='keras_tensor')\n",
+            "Output Type:\n",
+            "  TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)\n",
+            "Captures:\n",
+            "  137583387480080: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387482960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387483536: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387480848: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387485264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387486032: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387486992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387486416: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387485072: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137583387488336: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
+          ]
+        }
+      ],
+      "source": [
+        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+        "tflite_model = converter.convert()\n",
+        "\n",
+        "# Save the model.\n",
+        "with open('model_new.tflite', 'wb') as f:\n",
+        "  f.write(tflite_model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NipaU1k91LFL",
+        "outputId": "af0ffbac-ebb8-417f-cfc3-0f8c73f7041a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting keras-tuner\n",
+            "  Downloading keras_tuner-1.4.7-py3-none-any.whl.metadata (5.4 kB)\n",
+            "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (from keras-tuner) (3.8.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from keras-tuner) (24.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from keras-tuner) (2.32.3)\n",
+            "Collecting kt-legacy (from keras-tuner)\n",
+            "  Downloading kt_legacy-1.0.5-py3-none-any.whl.metadata (221 bytes)\n",
+            "Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (1.4.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (2.0.2)\n",
+            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (13.9.4)\n",
+            "Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (0.0.8)\n",
+            "Requirement already satisfied: h5py in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (3.12.1)\n",
+            "Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (0.14.1)\n",
+            "Requirement already satisfied: ml-dtypes in /usr/local/lib/python3.11/dist-packages (from keras->keras-tuner) (0.4.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->keras-tuner) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->keras-tuner) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->keras-tuner) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->keras-tuner) (2025.1.31)\n",
+            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.11/dist-packages (from optree->keras->keras-tuner) (4.12.2)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras->keras-tuner) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras->keras-tuner) (2.18.0)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras->keras-tuner) (0.1.2)\n",
+            "Downloading keras_tuner-1.4.7-py3-none-any.whl (129 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.1/129.1 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n",
+            "Installing collected packages: kt-legacy, keras-tuner\n",
+            "Successfully installed keras-tuner-1.4.7 kt-legacy-1.0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install keras-tuner"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sAxn6eNV1O48"
+      },
+      "outputs": [],
+      "source": [
+        "import keras_tuner as kt\n",
+        "from tensorflow.keras.regularizers import l2\n",
+        "\n",
+        "def build_model(hp):\n",
+        "    model = Sequential()\n",
+        "\n",
+        "    model.add(Conv1D(\n",
+        "        filters=hp.Choice('filters_1', values=[32, 64, 128]),\n",
+        "        kernel_size=hp.Choice('kernel_size_1', values=[2, 3, 5]),\n",
+        "        activation='relu',\n",
+        "        name='Conv1D_1',\n",
+        "        input_shape=(18, 1)\n",
+        "    ))\n",
+        "\n",
+        "    model.add(MaxPooling1D(pool_size=2, name='MaxPooling1D_1'))\n",
+        "\n",
+        "    model.add(Conv1D(\n",
+        "        filters=hp.Choice('filters_2', values=[32, 64]),\n",
+        "        kernel_size=hp.Choice('kernel_size_2', values=[2, 3]),\n",
+        "        activation='relu',\n",
+        "        name='Conv1D_2'\n",
+        "    ))\n",
+        "\n",
+        "    model.add(Flatten(name='Flatten'))\n",
+        "\n",
+        "    # Fully Connected Layers\n",
+        "    model.add(Dense(\n",
+        "        units=hp.Choice('dense_1', values=[32, 64, 128]),\n",
+        "        activation='relu',\n",
+        "        name='Dense_1',\n",
+        "        kernel_regularizer=l2(hp.Choice('l2_conv1', values=[1e-4, 1e-3, 1e-2]))\n",
+        "    ))\n",
+        "\n",
+        "    model.add(Dense(\n",
+        "        units=hp.Choice('dense_2', values=[16, 32, 64]),\n",
+        "        activation='relu',\n",
+        "        name='Dense_2',\n",
+        "        kernel_regularizer=l2(hp.Choice('l2_conv2', values=[1e-4, 1e-3, 1e-2]))\n",
+        "    ))\n",
+        "\n",
+        "    model.add(Dense(1, name='Output'))\n",
+        "\n",
+        "    # Optimizer tuning\n",
+        "    optimizer = hp.Choice('optimizer', values=['adam','rmsprop'])\n",
+        "    model.compile(\n",
+        "        optimizer=optimizer,\n",
+        "        loss=MeanSquaredError(),\n",
+        "        metrics=[tf.keras.metrics.R2Score()]\n",
+        "    )\n",
+        "\n",
+        "    return model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A5-2_giq388N",
+        "outputId": "b48537b5-06d7-4854-c70d-a2bd43c92e32"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trial 10 Complete [00h 00m 20s]\n",
+            "val_r2_score: 0.5524754524230957\n",
+            "\n",
+            "Best val_r2_score So Far: 0.15453773736953735\n",
+            "Total elapsed time: 00h 04m 35s\n",
+            "\n",
+            "Best Hyperparameters:\n",
+            "- Filters 1: 64\n",
+            "- Kernel Size 1: 5\n",
+            "- Filters 2: 32\n",
+            "- Kernel Size 2: 3\n",
+            "- Dense Layer 1: 32\n",
+            "- Dense Layer 2: 16\n",
+            "- Optimizer: adam\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "tuner = kt.RandomSearch(\n",
+        "    build_model,\n",
+        "    objective='val_r2_score',\n",
+        "    max_trials=10,  # Jumlah kombinasi hyperparameter yang diuji\n",
+        "    executions_per_trial=2,\n",
+        "    directory='tuning_dir',\n",
+        "    project_name='cnn_tuning'\n",
+        ")\n",
+        "\n",
+        "tuner.search(train_ds, epochs=300, validation_data=test_ds,callbacks=callbacks)\n",
+        "\n",
+        "# Ambil model terbaik\n",
+        "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+        "\n",
+        "print(f\"\"\"\n",
+        "Best Hyperparameters:\n",
+        "- Filters 1: {best_hps.get('filters_1')}\n",
+        "- Kernel Size 1: {best_hps.get('kernel_size_1')}\n",
+        "- Filters 2: {best_hps.get('filters_2')}\n",
+        "- Kernel Size 2: {best_hps.get('kernel_size_2')}\n",
+        "- Dense Layer 1: {best_hps.get('dense_1')}\n",
+        "- Dense Layer 2: {best_hps.get('dense_2')}\n",
+        "- Optimizer: {best_hps.get('optimizer')}\n",
+        "\"\"\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "-0XqbxAVVcgd"
+      },
+      "outputs": [],
+      "source": [
+        "tuner_results = tuner.get_best_hyperparameters(num_trials=5)\n",
+        "results_data = []\n",
+        "for hps in tuner_results:\n",
+        "    results_data.append({\n",
+        "        'filters_1': hps.get('filters_1'),\n",
+        "        'kernel_size_1': hps.get('kernel_size_1'),\n",
+        "        'filters_2': hps.get('filters_2'),\n",
+        "        'kernel_size_2': hps.get('kernel_size_2'),\n",
+        "        'dense_1': hps.get('dense_1'),\n",
+        "        'dense_2': hps.get('dense_2'),\n",
+        "        'optimizer': hps.get('optimizer'),\n",
+        "        # 'r2_score': hps.get('val_r2_score')  # Ambil R² score dari validasi\n",
+        "    })\n",
+        "results_df = pd.DataFrame(results_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "WJx3IbydXFI1",
+        "outputId": "8d9c1273-58b7-4bf6-a9a4-96fde6f0ff79"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"filters_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48,\n        \"min\": 32,\n        \"max\": 128,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          64,\n          128,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kernel_size_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filters_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 32,\n        \"max\": 64,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          64,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kernel_size_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dense_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 32,\n        \"max\": 64,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          64,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dense_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 16,\n        \"max\": 64,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"optimizer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"adam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "results_df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7a2b3aeb-70d1-4824-b373-b7f3f22a002c\" 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>filters_1</th>\n",
+              "      <th>kernel_size_1</th>\n",
+              "      <th>filters_2</th>\n",
+              "      <th>kernel_size_2</th>\n",
+              "      <th>dense_1</th>\n",
+              "      <th>dense_2</th>\n",
+              "      <th>optimizer</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>5</td>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "      <td>32</td>\n",
+              "      <td>16</td>\n",
+              "      <td>adam</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>128</td>\n",
+              "      <td>3</td>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "      <td>32</td>\n",
+              "      <td>32</td>\n",
+              "      <td>adam</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>32</td>\n",
+              "      <td>3</td>\n",
+              "      <td>32</td>\n",
+              "      <td>2</td>\n",
+              "      <td>64</td>\n",
+              "      <td>32</td>\n",
+              "      <td>adam</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>32</td>\n",
+              "      <td>2</td>\n",
+              "      <td>32</td>\n",
+              "      <td>2</td>\n",
+              "      <td>64</td>\n",
+              "      <td>64</td>\n",
+              "      <td>adam</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>128</td>\n",
+              "      <td>3</td>\n",
+              "      <td>64</td>\n",
+              "      <td>2</td>\n",
+              "      <td>32</td>\n",
+              "      <td>64</td>\n",
+              "      <td>adam</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-7a2b3aeb-70d1-4824-b373-b7f3f22a002c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\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",
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+              "      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-7a2b3aeb-70d1-4824-b373-b7f3f22a002c 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-7a2b3aeb-70d1-4824-b373-b7f3f22a002c');\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-98635d4b-2ac5-4553-b7f6-3bff78e9f85b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98635d4b-2ac5-4553-b7f6-3bff78e9f85b')\"\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-98635d4b-2ac5-4553-b7f6-3bff78e9f85b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_6da995d7-1bc9-4e89-a074-fa8444012d28\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_6da995d7-1bc9-4e89-a074-fa8444012d28 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   filters_1  kernel_size_1  filters_2  kernel_size_2  dense_1  dense_2  \\\n",
+              "0         64              5         32              3       32       16   \n",
+              "1        128              3         32              3       32       32   \n",
+              "2         32              3         32              2       64       32   \n",
+              "3         32              2         32              2       64       64   \n",
+              "4        128              3         64              2       32       64   \n",
+              "\n",
+              "  optimizer  \n",
+              "0      adam  \n",
+              "1      adam  \n",
+              "2      adam  \n",
+              "3      adam  \n",
+              "4      adam  "
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ePIVoWJE1Srz",
+        "outputId": "c2de196c-103f-4c01-ed9b-bb08396a1129"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/300\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: 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__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 40ms/step - loss: 0.3458 - r2_score: 0.0456 - val_loss: 0.2446 - val_r2_score: 0.2706 - learning_rate: 0.0010\n",
+            "Epoch 2/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2258 - r2_score: 0.3981 - val_loss: 0.1466 - val_r2_score: 0.5946 - learning_rate: 0.0010\n",
+            "Epoch 3/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1340 - r2_score: 0.6691 - val_loss: 0.0944 - val_r2_score: 0.7665 - learning_rate: 0.0010\n",
+            "Epoch 4/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0930 - r2_score: 0.7917 - val_loss: 0.0820 - val_r2_score: 0.8061 - learning_rate: 0.0010\n",
+            "Epoch 5/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0680 - r2_score: 0.8661 - val_loss: 0.0683 - val_r2_score: 0.8499 - learning_rate: 0.0010\n",
+            "Epoch 6/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0519 - r2_score: 0.9136 - val_loss: 0.0609 - val_r2_score: 0.8731 - learning_rate: 0.0010\n",
+            "Epoch 7/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0404 - r2_score: 0.9470 - val_loss: 0.0520 - val_r2_score: 0.9012 - learning_rate: 0.0010\n",
+            "Epoch 8/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0361 - r2_score: 0.9585 - val_loss: 0.0518 - val_r2_score: 0.9004 - learning_rate: 0.0010\n",
+            "Epoch 9/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0317 - r2_score: 0.9706 - val_loss: 0.0476 - val_r2_score: 0.9129 - learning_rate: 0.0010\n",
+            "Epoch 10/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0297 - r2_score: 0.9750 - val_loss: 0.0454 - val_r2_score: 0.9189 - learning_rate: 0.0010\n",
+            "Epoch 11/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0277 - r2_score: 0.9796 - val_loss: 0.0437 - val_r2_score: 0.9233 - learning_rate: 0.0010\n",
+            "Epoch 12/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0263 - r2_score: 0.9828 - val_loss: 0.0411 - val_r2_score: 0.9304 - learning_rate: 0.0010\n",
+            "Epoch 13/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0248 - r2_score: 0.9859 - val_loss: 0.0396 - val_r2_score: 0.9341 - learning_rate: 0.0010\n",
+            "Epoch 14/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0234 - r2_score: 0.9890 - val_loss: 0.0384 - val_r2_score: 0.9370 - learning_rate: 0.0010\n",
+            "Epoch 15/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0222 - r2_score: 0.9915 - val_loss: 0.0368 - val_r2_score: 0.9412 - learning_rate: 0.0010\n",
+            "Epoch 16/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0214 - r2_score: 0.9928 - val_loss: 0.0372 - val_r2_score: 0.9387 - learning_rate: 0.0010\n",
+            "Epoch 17/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0205 - r2_score: 0.9945 - val_loss: 0.0364 - val_r2_score: 0.9402 - learning_rate: 0.0010\n",
+            "Epoch 18/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0199 - r2_score: 0.9954 - val_loss: 0.0362 - val_r2_score: 0.9396 - learning_rate: 0.0010\n",
+            "Epoch 19/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0194 - r2_score: 0.9960 - val_loss: 0.0354 - val_r2_score: 0.9413 - learning_rate: 0.0010\n",
+            "Epoch 20/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0189 - r2_score: 0.9963 - val_loss: 0.0350 - val_r2_score: 0.9418 - learning_rate: 0.0010\n",
+            "Epoch 21/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0185 - r2_score: 0.9967 - val_loss: 0.0349 - val_r2_score: 0.9409 - learning_rate: 0.0010\n",
+            "Epoch 22/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0182 - r2_score: 0.9968 - val_loss: 0.0338 - val_r2_score: 0.9438 - learning_rate: 0.0010\n",
+            "Epoch 23/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0178 - r2_score: 0.9971 - val_loss: 0.0337 - val_r2_score: 0.9430 - learning_rate: 0.0010\n",
+            "Epoch 24/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0175 - r2_score: 0.9972 - val_loss: 0.0333 - val_r2_score: 0.9436 - learning_rate: 0.0010\n",
+            "Epoch 25/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0172 - r2_score: 0.9973 - val_loss: 0.0325 - val_r2_score: 0.9452 - learning_rate: 0.0010\n",
+            "Epoch 26/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0169 - r2_score: 0.9973 - val_loss: 0.0325 - val_r2_score: 0.9445 - learning_rate: 0.0010\n",
+            "Epoch 27/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0166 - r2_score: 0.9973 - val_loss: 0.0313 - val_r2_score: 0.9476 - learning_rate: 0.0010\n",
+            "Epoch 28/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0162 - r2_score: 0.9978 - val_loss: 0.0310 - val_r2_score: 0.9475 - learning_rate: 0.0010\n",
+            "Epoch 29/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0159 - r2_score: 0.9979 - val_loss: 0.0306 - val_r2_score: 0.9482 - learning_rate: 0.0010\n",
+            "Epoch 30/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0156 - r2_score: 0.9980 - val_loss: 0.0298 - val_r2_score: 0.9501 - learning_rate: 0.0010\n",
+            "Epoch 31/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0154 - r2_score: 0.9981 - val_loss: 0.0298 - val_r2_score: 0.9494 - learning_rate: 0.0010\n",
+            "Epoch 32/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0151 - r2_score: 0.9982 - val_loss: 0.0290 - val_r2_score: 0.9512 - learning_rate: 0.0010\n",
+            "Epoch 33/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0148 - r2_score: 0.9984 - val_loss: 0.0289 - val_r2_score: 0.9508 - learning_rate: 0.0010\n",
+            "Epoch 34/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0146 - r2_score: 0.9984 - val_loss: 0.0288 - val_r2_score: 0.9503 - learning_rate: 0.0010\n",
+            "Epoch 35/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0143 - r2_score: 0.9985 - val_loss: 0.0279 - val_r2_score: 0.9528 - learning_rate: 0.0010\n",
+            "Epoch 36/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0141 - r2_score: 0.9987 - val_loss: 0.0278 - val_r2_score: 0.9523 - learning_rate: 0.0010\n",
+            "Epoch 37/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0138 - r2_score: 0.9987 - val_loss: 0.0275 - val_r2_score: 0.9527 - learning_rate: 0.0010\n",
+            "Epoch 38/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0136 - r2_score: 0.9987 - val_loss: 0.0275 - val_r2_score: 0.9519 - learning_rate: 0.0010\n",
+            "Epoch 39/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0134 - r2_score: 0.9988 - val_loss: 0.0272 - val_r2_score: 0.9523 - learning_rate: 0.0010\n",
+            "Epoch 40/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0132 - r2_score: 0.9990 - val_loss: 0.0264 - val_r2_score: 0.9545 - learning_rate: 0.0010\n",
+            "Epoch 41/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0130 - r2_score: 0.9989 - val_loss: 0.0269 - val_r2_score: 0.9521 - learning_rate: 0.0010\n",
+            "Epoch 42/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0128 - r2_score: 0.9990 - val_loss: 0.0269 - val_r2_score: 0.9516 - learning_rate: 0.0010\n",
+            "Epoch 43/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0126 - r2_score: 0.9991 - val_loss: 0.0257 - val_r2_score: 0.9547 - learning_rate: 0.0010\n",
+            "Epoch 44/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0124 - r2_score: 0.9990 - val_loss: 0.0265 - val_r2_score: 0.9516 - learning_rate: 0.0010\n",
+            "Epoch 45/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0122 - r2_score: 0.9991 - val_loss: 0.0260 - val_r2_score: 0.9527 - learning_rate: 0.0010\n",
+            "Epoch 46/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0120 - r2_score: 0.9992 - val_loss: 0.0255 - val_r2_score: 0.9538 - learning_rate: 0.0010\n",
+            "Epoch 47/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118 - r2_score: 0.9992 - val_loss: 0.0251 - val_r2_score: 0.9547 - learning_rate: 0.0010\n",
+            "Epoch 48/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0117 - r2_score: 0.9991 - val_loss: 0.0262 - val_r2_score: 0.9506 - learning_rate: 0.0010\n",
+            "Epoch 49/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0115 - r2_score: 0.9993 - val_loss: 0.0251 - val_r2_score: 0.9535 - learning_rate: 0.0010\n",
+            "Epoch 50/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0114 - r2_score: 0.9993 - val_loss: 0.0252 - val_r2_score: 0.9526 - learning_rate: 0.0010\n",
+            "Epoch 51/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0112 - r2_score: 0.9993 - val_loss: 0.0246 - val_r2_score: 0.9543 - learning_rate: 0.0010\n",
+            "Epoch 52/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0110 - r2_score: 0.9993 - val_loss: 0.0255 - val_r2_score: 0.9509 - learning_rate: 0.0010\n",
+            "Epoch 53/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0109 - r2_score: 0.9993 - val_loss: 0.0244 - val_r2_score: 0.9539 - learning_rate: 0.0010\n",
+            "Epoch 54/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0108 - r2_score: 0.9993 - val_loss: 0.0253 - val_r2_score: 0.9506 - learning_rate: 0.0010\n",
+            "Epoch 55/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0106 - r2_score: 0.9993 - val_loss: 0.0238 - val_r2_score: 0.9550 - learning_rate: 0.0010\n",
+            "Epoch 56/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0106 - r2_score: 0.9990 - val_loss: 0.0261 - val_r2_score: 0.9470 - learning_rate: 0.0010\n",
+            "Epoch 57/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0105 - r2_score: 0.9989 - val_loss: 0.0228 - val_r2_score: 0.9575 - learning_rate: 0.0010\n",
+            "Epoch 58/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0107 - r2_score: 0.9978 - val_loss: 0.0272 - val_r2_score: 0.9423 - learning_rate: 0.0010\n",
+            "Epoch 59/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0107 - r2_score: 0.9974 - val_loss: 0.0212 - val_r2_score: 0.9619 - learning_rate: 0.0010\n",
+            "Epoch 60/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0116 - r2_score: 0.9942 - val_loss: 0.0314 - val_r2_score: 0.9277 - learning_rate: 0.0010\n",
+            "Epoch 61/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119 - r2_score: 0.9930 - val_loss: 0.0202 - val_r2_score: 0.9643 - learning_rate: 0.0010\n",
+            "Epoch 62/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0128 - r2_score: 0.9901 - val_loss: 0.0311 - val_r2_score: 0.9279 - learning_rate: 0.0010\n",
+            "Epoch 63/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0115 - r2_score: 0.9937 - val_loss: 0.0224 - val_r2_score: 0.9563 - learning_rate: 0.0010\n",
+            "Epoch 64/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0100 - r2_score: 0.9977 - val_loss: 0.0233 - val_r2_score: 0.9530 - learning_rate: 0.0010\n",
+            "Epoch 65/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0097 - r2_score: 0.9982 - val_loss: 0.0261 - val_r2_score: 0.9434 - learning_rate: 0.0010\n",
+            "Epoch 66/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0100 - r2_score: 0.9967\n",
+            "Epoch 66: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0098 - r2_score: 0.9977 - val_loss: 0.0216 - val_r2_score: 0.9578 - learning_rate: 0.0010\n",
+            "Epoch 67/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0094 - r2_score: 0.9985 - val_loss: 0.0223 - val_r2_score: 0.9555 - learning_rate: 1.0000e-04\n",
+            "Epoch 68/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0092 - r2_score: 0.9992 - val_loss: 0.0227 - val_r2_score: 0.9540 - learning_rate: 1.0000e-04\n",
+            "Epoch 69/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0091 - r2_score: 0.9995 - val_loss: 0.0230 - val_r2_score: 0.9532 - learning_rate: 1.0000e-04\n",
+            "Epoch 70/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0230 - val_r2_score: 0.9530 - learning_rate: 1.0000e-04\n",
+            "Epoch 71/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0090 - r2_score: 0.9995\n",
+            "Epoch 71: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-04\n",
+            "Epoch 72/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-05\n",
+            "Epoch 73/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-05\n",
+            "Epoch 74/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-05\n",
+            "Epoch 75/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-05\n",
+            "Epoch 76/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0090 - r2_score: 0.9995\n",
+            "Epoch 76: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-05\n",
+            "Epoch 77/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 78/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 79/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 80/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 81/300\n",
+            "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9995\n",
+            "Epoch 81: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 82/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 83/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9996 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 84/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 85/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 86/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 87/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9532 - learning_rate: 1.0000e-06\n",
+            "Epoch 88/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-06\n",
+            "Epoch 89/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-06\n",
+            "Epoch 90/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-06\n",
+            "Epoch 91/300\n",
+            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0090 - r2_score: 0.9997 - val_loss: 0.0229 - val_r2_score: 0.9533 - learning_rate: 1.0000e-06\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7d20dedb1350>"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "best_model = tuner.hypermodel.build(best_hps)\n",
+        "best_model.fit(train_ds, epochs=300, validation_data=test_ds,callbacks=callbacks)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 504
+        },
+        "id": "uDv-AogWGoDx",
+        "outputId": "d12cfcdb-5119-40f0-bccc-a598cbc9dd53"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import numpy as np\n",
+        "\n",
+        "# Prediksi hasil dengan model yang sudah dilatih\n",
+        "y_pred = best_model.predict(X_test)\n",
+        "y_pred_inv = inverse_transform_y(y_pred)  # Invers transformasi jika diperlukan\n",
+        "y_test_inv = inverse_transform_y(y_test)\n",
+        "# Plot scatter Actual vs Predicted\n",
+        "plt.figure(figsize=(6, 5))\n",
+        "sns.scatterplot(x=y_test_inv.flatten(), y=y_pred_inv.flatten(), alpha=0.7)\n",
+        "\n",
+        "# Garis referensi y = x (ideal jika prediksi sempurna)\n",
+        "min_val = min(y_test_inv.min(), y_pred_inv.min())\n",
+        "max_val = max(y_test_inv.max(), y_pred_inv.max())\n",
+        "plt.plot([min_val, max_val], [min_val, max_val], linestyle=\"--\", color=\"red\")\n",
+        "\n",
+        "# Label dan judul\n",
+        "plt.xlabel(\"Actual Values\")\n",
+        "plt.ylabel(\"Predicted Values\")\n",
+        "plt.title(\"Actual vs Predicted Values HyperParameter Tuning\")\n",
+        "\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QoDC0HhMChl_",
+        "outputId": "fa03f43c-62ed-48b1-a7b6-67d686507fc0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "R-squared after tuning: 0.9643\n",
+            "MSE after tuning: 0.0107\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import r2_score\n",
+        "y_pred = best_model.predict(X_test)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "print(f'R-squared after tuning: {r2:.4f}')\n",
+        "msE = mean_squared_error(y_test, y_pred)\n",
+        "print(f'MSE after tuning: {msE:.4f}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yA-vXeHhxF2P",
+        "outputId": "5e79c397-3638-4298-b57e-eafdc9beeda4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_preds = []\n",
+        "for i in range(120):\n",
+        "    X_pred = df.iloc[i, :-1].values  # Ambil fitur dari setiap baris\n",
+        "    X_pred = scaler_X.transform(X_pred.reshape(1, -1))  # Normalisasi\n",
+        "    X_pred = X_pred.reshape(1, 18, 1)  # Ubah ke bentuk yang sesuai untuk model\n",
+        "\n",
+        "    y_pred = best_model.predict(X_pred)  # Prediksi dengan model terbaik\n",
+        "    y_pred = inverse_transform_y(y_pred)  # Invers transformasi jika diperlukan\n",
+        "\n",
+        "    # Pastikan bentuk y_pred adalah (1, 1) sebelum mengakses indeks\n",
+        "    y_preds.append(y_pred.item())  # Gunakan .item() untuk mengambil nilai skalar\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7NSdaZNqxKdq",
+        "outputId": "5db59de5-193d-426e-d39f-b7a7d9548768"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data 1: Prediksi = 101.07846069335938\n",
+            "Data 2: Prediksi = 101.07846069335938\n",
+            "Data 3: Prediksi = 101.07846069335938\n",
+            "Data 4: Prediksi = 101.07846069335938\n",
+            "Data 5: Prediksi = 101.07846069335938\n",
+            "Data 6: Prediksi = 101.07846069335938\n",
+            "Data 7: Prediksi = 101.07846069335938\n",
+            "Data 8: Prediksi = 101.5075454711914\n",
+            "Data 9: Prediksi = 101.5075454711914\n",
+            "Data 10: Prediksi = 101.5075454711914\n",
+            "Data 11: Prediksi = 101.5075454711914\n",
+            "Data 12: Prediksi = 86.57645416259766\n",
+            "Data 13: Prediksi = 102.24028015136719\n",
+            "Data 14: Prediksi = 102.24028015136719\n",
+            "Data 15: Prediksi = 102.24028015136719\n",
+            "Data 16: Prediksi = 101.4759521484375\n",
+            "Data 17: Prediksi = 101.5075454711914\n",
+            "Data 18: Prediksi = 101.60734558105469\n",
+            "Data 19: Prediksi = 101.60734558105469\n",
+            "Data 20: Prediksi = 101.60734558105469\n",
+            "Data 21: Prediksi = 89.59921264648438\n",
+            "Data 22: Prediksi = 89.59921264648438\n",
+            "Data 23: Prediksi = 90.12796783447266\n",
+            "Data 24: Prediksi = 90.12796783447266\n",
+            "Data 25: Prediksi = 89.03561401367188\n",
+            "Data 26: Prediksi = 89.11809539794922\n",
+            "Data 27: Prediksi = 90.18357849121094\n",
+            "Data 28: Prediksi = 90.79273223876953\n",
+            "Data 29: Prediksi = 91.4521255493164\n",
+            "Data 30: Prediksi = 90.18357849121094\n",
+            "Data 31: Prediksi = 91.4521255493164\n",
+            "Data 32: Prediksi = 89.90308380126953\n",
+            "Data 33: Prediksi = 90.18357849121094\n",
+            "Data 34: Prediksi = 90.12796783447266\n",
+            "Data 35: Prediksi = 90.18357849121094\n",
+            "Data 36: Prediksi = 89.90308380126953\n",
+            "Data 37: Prediksi = 90.18357849121094\n",
+            "Data 38: Prediksi = 90.18357849121094\n",
+            "Data 39: Prediksi = 90.18357849121094\n",
+            "Data 40: Prediksi = 90.18357849121094\n",
+            "Data 41: Prediksi = 83.5122299194336\n",
+            "Data 42: Prediksi = 80.74687957763672\n",
+            "Data 43: Prediksi = 80.74687957763672\n",
+            "Data 44: Prediksi = 74.29013061523438\n",
+            "Data 45: Prediksi = 80.2178726196289\n",
+            "Data 46: Prediksi = 80.74687957763672\n",
+            "Data 47: Prediksi = 80.34146881103516\n",
+            "Data 48: Prediksi = 80.8704833984375\n",
+            "Data 49: Prediksi = 81.42855072021484\n",
+            "Data 50: Prediksi = 81.42855072021484\n",
+            "Data 51: Prediksi = 80.93084716796875\n",
+            "Data 52: Prediksi = 80.93084716796875\n",
+            "Data 53: Prediksi = 80.91128540039062\n",
+            "Data 54: Prediksi = 81.270263671875\n",
+            "Data 55: Prediksi = 81.42855072021484\n",
+            "Data 56: Prediksi = 81.7679672241211\n",
+            "Data 57: Prediksi = 81.27751159667969\n",
+            "Data 58: Prediksi = 80.09344482421875\n",
+            "Data 59: Prediksi = 80.27143096923828\n",
+            "Data 60: Prediksi = 80.34883117675781\n",
+            "Data 61: Prediksi = 71.7040786743164\n",
+            "Data 62: Prediksi = 71.62554168701172\n",
+            "Data 63: Prediksi = 66.95866394042969\n",
+            "Data 64: Prediksi = 73.02384185791016\n",
+            "Data 65: Prediksi = 71.7190933227539\n",
+            "Data 66: Prediksi = 71.7190933227539\n",
+            "Data 67: Prediksi = 71.7040786743164\n",
+            "Data 68: Prediksi = 71.7190933227539\n",
+            "Data 69: Prediksi = 71.89790344238281\n",
+            "Data 70: Prediksi = 71.1645278930664\n",
+            "Data 71: Prediksi = 69.8851089477539\n",
+            "Data 72: Prediksi = 70.63230895996094\n",
+            "Data 73: Prediksi = 70.63230895996094\n",
+            "Data 74: Prediksi = 70.63230895996094\n",
+            "Data 75: Prediksi = 70.39860534667969\n",
+            "Data 76: Prediksi = 71.35065460205078\n",
+            "Data 77: Prediksi = 71.78302001953125\n",
+            "Data 78: Prediksi = 71.35065460205078\n",
+            "Data 79: Prediksi = 72.54496765136719\n",
+            "Data 80: Prediksi = 71.35065460205078\n",
+            "Data 81: Prediksi = 60.63755416870117\n",
+            "Data 82: Prediksi = 60.44389343261719\n",
+            "Data 83: Prediksi = 60.44389343261719\n",
+            "Data 84: Prediksi = 60.44389343261719\n",
+            "Data 85: Prediksi = 62.077232360839844\n",
+            "Data 86: Prediksi = 60.30274963378906\n",
+            "Data 87: Prediksi = 60.30274963378906\n",
+            "Data 88: Prediksi = 62.537574768066406\n",
+            "Data 89: Prediksi = 58.80356216430664\n",
+            "Data 90: Prediksi = 60.30274963378906\n",
+            "Data 91: Prediksi = 60.30274963378906\n",
+            "Data 92: Prediksi = 60.30274963378906\n",
+            "Data 93: Prediksi = 60.30274963378906\n",
+            "Data 94: Prediksi = 60.30274963378906\n",
+            "Data 95: Prediksi = 60.48006057739258\n",
+            "Data 96: Prediksi = 62.07733917236328\n",
+            "Data 97: Prediksi = 62.07733917236328\n",
+            "Data 98: Prediksi = 62.07733917236328\n",
+            "Data 99: Prediksi = 61.66597366333008\n",
+            "Data 100: Prediksi = 61.66597366333008\n",
+            "Data 101: Prediksi = 50.909629821777344\n",
+            "Data 102: Prediksi = 50.909629821777344\n",
+            "Data 103: Prediksi = 50.909629821777344\n",
+            "Data 104: Prediksi = 50.14573669433594\n",
+            "Data 105: Prediksi = 50.660118103027344\n",
+            "Data 106: Prediksi = 50.63202667236328\n",
+            "Data 107: Prediksi = 50.63202667236328\n",
+            "Data 108: Prediksi = 53.132904052734375\n",
+            "Data 109: Prediksi = 51.07947540283203\n",
+            "Data 110: Prediksi = 50.63202667236328\n",
+            "Data 111: Prediksi = 50.65995788574219\n",
+            "Data 112: Prediksi = 50.65995788574219\n",
+            "Data 113: Prediksi = 50.63202667236328\n",
+            "Data 114: Prediksi = 50.63202667236328\n",
+            "Data 115: Prediksi = 50.966590881347656\n",
+            "Data 116: Prediksi = 50.966590881347656\n",
+            "Data 117: Prediksi = 50.47744369506836\n",
+            "Data 118: Prediksi = 50.909629821777344\n",
+            "Data 119: Prediksi = 50.909629821777344\n",
+            "Data 120: Prediksi = 50.909629821777344\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i, pred in enumerate(y_preds):\n",
+        "    print(f\"Data {i+1}: Prediksi = {pred}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vuFqLL7EDBPA"
+      },
+      "source": [
+        "# SAVE MODEL HYPERPARAMETER TUNING"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gtO-v8_QCs1o",
+        "outputId": "5917d284-317d-4b08-8ab2-c2e8f34fc734"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        }
+      ],
+      "source": [
+        "best_model.save(\"model_tuning_spektroskopi_new.h5\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "P6IARMCTDKvB",
+        "outputId": "869a1503-8c37-4377-90e9-36e456102547"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saved artifact at '/tmp/tmp1smyn5hn'. The following endpoints are available:\n",
+            "\n",
+            "* Endpoint 'serve'\n",
+            "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 18, 1), dtype=tf.float32, name='keras_tensor_29')\n",
+            "Output Type:\n",
+            "  TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)\n",
+            "Captures:\n",
+            "  137580131214224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131213264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131209040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131218448: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131218256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131219216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131219024: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131219984: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131219792: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  137580131220752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
+          ]
+        }
+      ],
+      "source": [
+        "converter = tf.lite.TFLiteConverter.from_keras_model(best_model)\n",
+        "tflite_model = converter.convert()\n",
+        "\n",
+        "# Save the model.\n",
+        "with open('model_tuning_spektroskopi_new.tflite', 'wb') as f:\n",
+        "  f.write(tflite_model)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "62m_2GsxJd5Y",
+        "ntt8Fm0oJlsA",
+        "ni4PUz_RJpXA",
+        "XGhTGZrsJq54",
+        "Jt2z7M0VJsjE",
+        "Vm3hPBGnJuQq",
+        "eM5dv9uLEkof",
+        "SKecpFA-E_YN",
+        "upxFtTh9FXDa",
+        "RqfZzV18Fb_V",
+        "t1C1TKtpFdWd",
+        "pdwf5MvaFfAu",
+        "sejbGW1KE2cU"
+      ],
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Uji_model_skripsi (1).ipynb b/Uji_model_skripsi (1).ipynb
new file mode 100644
index 0000000..ed3ed47
--- /dev/null
+++ b/Uji_model_skripsi (1).ipynb	
@@ -0,0 +1,418 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "pA16t5i1KdzR"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.models import load_model\n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
+        "import joblib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xIsmloYVK0LT",
+        "outputId": "4e105a41-c4ba-4bd8-9ae8-a5c88ca50f53"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 1. Muat Model yang Telah Dilatih\n",
+        "model = load_model('model_new.h5')\n",
+        "model_tuning = load_model('model_tuning_spektroskopi_new.h5')\n",
+        "\n",
+        "scaler_X = joblib.load('scaler_X.pkl')\n",
+        "scaler_y = joblib.load('scaler_y.pkl')\n",
+        "\n",
+        "df = pd.read_csv('Fuel_All_External.csv')\n",
+        "X = df.iloc[:, :-1].values\n",
+        "y = df.iloc[:, -1].values\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 237,
+      "metadata": {
+        "id": "ZyT7wj4SmqZA"
+      },
+      "outputs": [],
+      "source": [
+        "def inverse_transform_y(predictions):\n",
+        "    return scaler_y.inverse_transform(predictions.reshape(-1, 1)).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 244,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OSKzZRPp4d00",
+        "outputId": "ba434853-5829-4c32-d109-df931e3a1b80"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shape X sebelum transform: (50, 18)\n",
+            "Scaler di-fit dengan jumlah fitur: 18\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Shape X sebelum transform:\", X.shape)\n",
+        "print(\"Scaler di-fit dengan jumlah fitur:\", scaler_X.n_features_in_)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M6m-JLHv1Srz",
+        "outputId": "d2bbefff-314a-4f7a-de7b-c30dd2c51946"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "X = df.iloc[:, :-1].values\n",
+        "y = df.iloc[:, -1].values\n",
+        "\n",
+        "X = scaler_X.transform(X)\n",
+        "y = scaler_y.transform(y.reshape(-1, 1)).flatten()\n",
+        "\n",
+        "# print(X.shape)\n",
+        "# print(y.shape)\n",
+        "y_preds = []\n",
+        "y_preds_tuning = []\n",
+        "for i in range(len(df)):\n",
+        "    # print(f\"Data {i+1}: X = {X[i]}, y = {y[i]}\")\n",
+        "    X_pred = X[i].reshape(1, 18, 1)\n",
+        "    y_pred = model.predict(X_pred)\n",
+        "    y_pred = inverse_transform_y(y_pred)\n",
+        "    y_pred_tuning = model_tuning.predict(X_pred)\n",
+        "    y_pred_tuning = inverse_transform_y(y_pred_tuning)\n",
+        "\n",
+        "    y_preds.append(y_pred)\n",
+        "    y_preds_tuning.append(y_pred_tuning)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9QsKHVhsANkK"
+      },
+      "source": [
+        "#Tanpa Tuning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 248,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LaACsdQj74wD",
+        "outputId": "bc88a503-f808-4348-f353-e67c09bdc97c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data 1: Prediksi = [84.23407]\n",
+            "Data 2: Prediksi = [84.23407]\n",
+            "Data 3: Prediksi = [84.23407]\n",
+            "Data 4: Prediksi = [84.23407]\n",
+            "Data 5: Prediksi = [84.23407]\n",
+            "Data 6: Prediksi = [84.23407]\n",
+            "Data 7: Prediksi = [84.23407]\n",
+            "Data 8: Prediksi = [84.23407]\n",
+            "Data 9: Prediksi = [90.308846]\n",
+            "Data 10: Prediksi = [90.308846]\n",
+            "Data 11: Prediksi = [80.63012]\n",
+            "Data 12: Prediksi = [80.63012]\n",
+            "Data 13: Prediksi = [80.63012]\n",
+            "Data 14: Prediksi = [80.63012]\n",
+            "Data 15: Prediksi = [80.63012]\n",
+            "Data 16: Prediksi = [80.63012]\n",
+            "Data 17: Prediksi = [80.63012]\n",
+            "Data 18: Prediksi = [80.63012]\n",
+            "Data 19: Prediksi = [80.63012]\n",
+            "Data 20: Prediksi = [80.63012]\n",
+            "Data 21: Prediksi = [71.96156]\n",
+            "Data 22: Prediksi = [71.96156]\n",
+            "Data 23: Prediksi = [70.68844]\n",
+            "Data 24: Prediksi = [70.68844]\n",
+            "Data 25: Prediksi = [70.68844]\n",
+            "Data 26: Prediksi = [70.68844]\n",
+            "Data 27: Prediksi = [70.68844]\n",
+            "Data 28: Prediksi = [70.68844]\n",
+            "Data 29: Prediksi = [70.68844]\n",
+            "Data 30: Prediksi = [70.68844]\n",
+            "Data 31: Prediksi = [69.318535]\n",
+            "Data 32: Prediksi = [69.318535]\n",
+            "Data 33: Prediksi = [69.318535]\n",
+            "Data 34: Prediksi = [69.318535]\n",
+            "Data 35: Prediksi = [69.318535]\n",
+            "Data 36: Prediksi = [69.39558]\n",
+            "Data 37: Prediksi = [69.39558]\n",
+            "Data 38: Prediksi = [69.39558]\n",
+            "Data 39: Prediksi = [69.318535]\n",
+            "Data 40: Prediksi = [69.318535]\n",
+            "Data 41: Prediksi = [54.984215]\n",
+            "Data 42: Prediksi = [54.984215]\n",
+            "Data 43: Prediksi = [54.984215]\n",
+            "Data 44: Prediksi = [54.984215]\n",
+            "Data 45: Prediksi = [54.984215]\n",
+            "Data 46: Prediksi = [50.889156]\n",
+            "Data 47: Prediksi = [50.889156]\n",
+            "Data 48: Prediksi = [50.889156]\n",
+            "Data 49: Prediksi = [54.984215]\n",
+            "Data 50: Prediksi = [54.984215]\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i, pred in enumerate(y_preds):\n",
+        "    print(f\"Data {i+1}: Prediksi = {pred}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eNA-rm20APn8"
+      },
+      "source": [
+        "#Tuning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 250,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pTknD6ehASj0",
+        "outputId": "a230eccc-f474-48a5-9e13-775b7367cfef"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data 1: Prediksi = [95.58472]\n",
+            "Data 2: Prediksi = [95.58472]\n",
+            "Data 3: Prediksi = [95.58472]\n",
+            "Data 4: Prediksi = [95.58472]\n",
+            "Data 5: Prediksi = [95.58472]\n",
+            "Data 6: Prediksi = [95.58472]\n",
+            "Data 7: Prediksi = [95.58472]\n",
+            "Data 8: Prediksi = [95.58472]\n",
+            "Data 9: Prediksi = [96.007904]\n",
+            "Data 10: Prediksi = [96.007904]\n",
+            "Data 11: Prediksi = [86.576454]\n",
+            "Data 12: Prediksi = [86.576454]\n",
+            "Data 13: Prediksi = [86.576454]\n",
+            "Data 14: Prediksi = [86.576454]\n",
+            "Data 15: Prediksi = [86.576454]\n",
+            "Data 16: Prediksi = [86.576454]\n",
+            "Data 17: Prediksi = [86.576454]\n",
+            "Data 18: Prediksi = [86.576454]\n",
+            "Data 19: Prediksi = [86.576454]\n",
+            "Data 20: Prediksi = [86.576454]\n",
+            "Data 21: Prediksi = [73.77576]\n",
+            "Data 22: Prediksi = [73.77576]\n",
+            "Data 23: Prediksi = [73.02384]\n",
+            "Data 24: Prediksi = [73.02384]\n",
+            "Data 25: Prediksi = [73.02384]\n",
+            "Data 26: Prediksi = [73.02384]\n",
+            "Data 27: Prediksi = [73.02384]\n",
+            "Data 28: Prediksi = [73.02384]\n",
+            "Data 29: Prediksi = [73.02384]\n",
+            "Data 30: Prediksi = [73.02384]\n",
+            "Data 31: Prediksi = [66.95867]\n",
+            "Data 32: Prediksi = [66.95867]\n",
+            "Data 33: Prediksi = [66.95867]\n",
+            "Data 34: Prediksi = [66.95867]\n",
+            "Data 35: Prediksi = [66.95867]\n",
+            "Data 36: Prediksi = [66.1415]\n",
+            "Data 37: Prediksi = [66.1415]\n",
+            "Data 38: Prediksi = [66.1415]\n",
+            "Data 39: Prediksi = [66.95867]\n",
+            "Data 40: Prediksi = [66.95867]\n",
+            "Data 41: Prediksi = [51.689125]\n",
+            "Data 42: Prediksi = [51.689125]\n",
+            "Data 43: Prediksi = [51.689125]\n",
+            "Data 44: Prediksi = [51.689125]\n",
+            "Data 45: Prediksi = [51.689125]\n",
+            "Data 46: Prediksi = [51.19272]\n",
+            "Data 47: Prediksi = [51.19272]\n",
+            "Data 48: Prediksi = [51.19272]\n",
+            "Data 49: Prediksi = [51.689125]\n",
+            "Data 50: Prediksi = [51.689125]\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i, pred in enumerate(y_preds_tuning):\n",
+        "    print(f\"Data {i+1}: Prediksi = {pred}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "78_9QUE4wZGK"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/detect2.py b/detect2.py
new file mode 100644
index 0000000..d575681
--- /dev/null
+++ b/detect2.py
@@ -0,0 +1,96 @@
+import tflite_runtime.interpreter as tflite
+import numpy as np
+import as7265x
+import smbus
+import joblib
+import RPi.GPIO as GPIO
+from luma.core.interface.serial import i2c
+from luma.core.render import canvas
+from luma.oled.device import ssd1306
+from time import sleep
+
+# ======================== Load TF Model ========================
+interpreter = tflite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+print("Input details:", input_details)
+print("Output details:", output_details)
+
+# ======================== Load Scalers ========================
+scaler_X = joblib.load('scaler_X.pkl')
+scaler_y = joblib.load('scaler_y.pkl')
+
+# ======================== GPIO Button Setup ========================
+BUTTON_PIN = 22  # Gunakan GPIO 22
+GPIO.setmode(GPIO.BCM)
+GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
+
+# ======================== Create Sensor Instance ========================
+i2c_bus = smbus.SMBus(1)
+sensor = as7265x.AS7265X(i2c_bus)
+
+# ======================== OLED Display Setup ========================
+serial = i2c(port=1, address=0x3C)
+device = ssd1306(serial, rotate=0)
+
+# ======================== Scan Function ========================
+def scan():
+    sensor.begin()
+    sensor.enableBulb(as7265x.LED_WHITE)
+    sensor.enableBulb(as7265x.LED_IR)
+    sensor.enableBulb(as7265x.LED_UV)
+    sensor.setIntegrationCycles(1)
+    sensor.takeMeasurements()
+
+    data = [
+        sensor.getCalibratedA(), sensor.getCalibratedB(), sensor.getCalibratedC(),
+        sensor.getCalibratedD(), sensor.getCalibratedE(), sensor.getCalibratedF(),
+        sensor.getCalibratedG(), sensor.getCalibratedH(), sensor.getCalibratedR(),
+        sensor.getCalibratedI(), sensor.getCalibratedS(), sensor.getCalibratedJ(),
+        sensor.getCalibratedT(), sensor.getCalibratedU(), sensor.getCalibratedV(),
+        sensor.getCalibratedW(), sensor.getCalibratedK(), sensor.getCalibratedL()
+    ]
+
+    sensor.disableBulb(as7265x.LED_WHITE)
+    sensor.disableBulb(as7265x.LED_IR)
+    sensor.disableBulb(as7265x.LED_UV)
+
+    data = np.array(data).reshape(1, -1)
+    return data
+
+# ======================== Predict Function ========================
+def predict(data):
+    scaled_data = scaler_X.transform(data)
+    scaled_data = scaled_data.reshape((1, 18, 1))
+
+    interpreter.set_tensor(input_details[0]['index'], scaled_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    output_original = scaler_y.inverse_transform(output_data)
+
+    print("Model output (scaled):", output_data)
+    print("Model output (original):", output_original)
+
+    with canvas(device) as draw:
+        draw.rectangle(device.bounding_box, outline="white", fill="black")
+        draw.text((5, 20), "Prediction Result:", fill="white")
+        draw.text((5, 40), str(np.round(output_original[0], 2)), fill="white")
+
+# ======================== Main Loop ========================
+print("Model berhasil diload")
+print("Tekan tombol untuk memulai prediksi...")
+
+try:
+    while True:
+        if GPIO.input(BUTTON_PIN) == GPIO.HIGH:
+            print("Tombol ditekan. Memulai scan dan prediksi...")
+            data = scan()
+            predict(data)
+            sleep(1.5)  # Debounce delay
+except KeyboardInterrupt:
+    print("Program dihentikan oleh pengguna.")
+finally:
+    GPIO.cleanup()
+    print("GPIO dibersihkan.")
diff --git a/model_new.h5 b/model_new.h5
new file mode 100644
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diff --git a/scaler_X.pkl b/scaler_X.pkl
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diff --git a/scaler_y.pkl b/scaler_y.pkl
new file mode 100644
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GIT binary patch
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diff --git a/scan_with_button.py b/scan_with_button.py
new file mode 100644
index 0000000..4dd2a24
--- /dev/null
+++ b/scan_with_button.py
@@ -0,0 +1,63 @@
+import RPi.GPIO as GPIO
+import time
+import subprocess
+import as7265x
+import smbus
+import numpy as np
+import os
+from datetime import datetime
+
+# Konfigurasi sensor
+i2c = smbus.SMBus(1)
+sensor = as7265x.AS7265X(i2c)
+sensor.begin()
+sensor.setIntegrationCycles(1)
+
+# Konfigurasi tombol
+BUTTON_PIN = 22  # Gunakan GPIO 22
+GPIO.setmode(GPIO.BCM)
+GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
+
+# Meminta input nama folder
+folder = input("Masukkan nama folder untuk menyimpan hasil: ")
+if not os.path.exists(folder):
+    os.makedirs(folder)
+
+# Fungsi untuk melakukan scanning
+def scan_and_save():
+    print("Scanning...")
+    sensor.enableBulb(as7265x.LED_WHITE)
+    sensor.enableBulb(as7265x.LED_IR)
+    sensor.enableBulb(as7265x.LED_UV)
+    
+    sensor.takeMeasurements()
+    data = [
+        sensor.getCalibratedA(), sensor.getCalibratedB(), sensor.getCalibratedC(),
+        sensor.getCalibratedD(), sensor.getCalibratedE(), sensor.getCalibratedF(),
+        sensor.getCalibratedG(), sensor.getCalibratedH(), sensor.getCalibratedR(),
+        sensor.getCalibratedI(), sensor.getCalibratedS(), sensor.getCalibratedJ(),
+        sensor.getCalibratedT(), sensor.getCalibratedU(), sensor.getCalibratedV(),
+        sensor.getCalibratedW(), sensor.getCalibratedK(), sensor.getCalibratedL()
+    ]
+    
+    sensor.disableBulb(as7265x.LED_WHITE)
+    sensor.disableBulb(as7265x.LED_IR)
+    sensor.disableBulb(as7265x.LED_UV)
+    
+    # Simpan hasil ke dalam file
+    filename = datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + ".txt"
+    file_path = os.path.join(folder, filename)
+    with open(file_path, "w") as file:
+        file.write(";".join(map(str, data)))
+    
+    print(f"Data telah disimpan di {file_path}")
+
+try:
+    print("Tekan tombol untuk memulai scanning...")
+    while True:
+        if GPIO.input(BUTTON_PIN) == GPIO.HIGH:
+            scan_and_save()
+            time.sleep(1)  # Hindari pemicuan berulang cepat
+except KeyboardInterrupt:
+    print("Keluar...")
+    GPIO.cleanup()