Code Skripsi
This is full code for prediction of fuel adulteration
This commit is contained in:
418
Uji_model_skripsi (1).ipynb
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418
Uji_model_skripsi (1).ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "pA16t5i1KdzR"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras.models import load_model\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
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"import joblib"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "xIsmloYVK0LT",
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"outputId": "4e105a41-c4ba-4bd8-9ae8-a5c88ca50f53"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"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"
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]
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}
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],
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"source": [
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"# 1. Muat Model yang Telah Dilatih\n",
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"model = load_model('model_new.h5')\n",
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"model_tuning = load_model('model_tuning_spektroskopi_new.h5')\n",
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"\n",
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"scaler_X = joblib.load('scaler_X.pkl')\n",
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"scaler_y = joblib.load('scaler_y.pkl')\n",
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"\n",
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"df = pd.read_csv('Fuel_All_External.csv')\n",
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"X = df.iloc[:, :-1].values\n",
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"y = df.iloc[:, -1].values\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 237,
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"metadata": {
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"id": "ZyT7wj4SmqZA"
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},
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"outputs": [],
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"source": [
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"def inverse_transform_y(predictions):\n",
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" return scaler_y.inverse_transform(predictions.reshape(-1, 1)).flatten()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 244,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "OSKzZRPp4d00",
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"outputId": "ba434853-5829-4c32-d109-df931e3a1b80"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Shape X sebelum transform: (50, 18)\n",
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"Scaler di-fit dengan jumlah fitur: 18\n"
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]
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}
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],
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"source": [
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"print(\"Shape X sebelum transform:\", X.shape)\n",
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"print(\"Scaler di-fit dengan jumlah fitur:\", scaler_X.n_features_in_)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "M6m-JLHv1Srz",
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"outputId": "d2bbefff-314a-4f7a-de7b-c30dd2c51946"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
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"\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
|
||||
}
|
||||
Reference in New Issue
Block a user