330 lines
182 KiB
Plaintext
330 lines
182 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "939c6588-f140-4b50-a0b8-d4a5a36fc86e",
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"metadata": {},
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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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"Dataset berhasil dimuat: (10000, 8)\n",
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"\n",
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"============================================================\n",
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"PROSES TRAINING DAN TUNING SEDANG BERJALAN...\n",
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"============================================================\n",
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">> Melatih KNN (Mencari K terbaik)...\n",
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" KNN Terbaik: {'classifier__n_neighbors': 3, 'classifier__weights': 'uniform'}\n",
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"\n",
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">> Melatih Random Forest...\n",
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" RF Terbaik: {'classifier__max_depth': None, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 100}\n",
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"\n",
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"============================================================\n",
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"HASIL EVALUASI AKHIR\n",
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"============================================================\n",
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"\n",
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"MODEL: KNN\n",
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"--------------------\n",
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" precision recall f1-score support\n",
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"\n",
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" Lewati 0.83 0.87 0.85 1494\n",
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" Tonton 0.55 0.46 0.50 506\n",
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"\n",
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" accuracy 0.77 2000\n",
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" macro avg 0.69 0.67 0.67 2000\n",
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"weighted avg 0.76 0.77 0.76 2000\n",
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"\n",
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"\n",
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"MODEL: Random Forest\n",
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"--------------------\n",
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" precision recall f1-score support\n",
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"\n",
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" Lewati 0.87 0.93 0.90 1494\n",
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" Tonton 0.74 0.60 0.66 506\n",
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"\n",
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" accuracy 0.85 2000\n",
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" macro avg 0.81 0.76 0.78 2000\n",
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"weighted avg 0.84 0.85 0.84 2000\n",
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"\n",
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"\n",
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"============================================================\n",
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"ANALISIS FITUR TERPENTING (Khusus Random Forest)\n",
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"(Catatan: KNN tidak memiliki fitur ini karena berbasis jarak)\n",
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"============================================================\n",
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"Menampilkan semua grafik evaluasi...\n"
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]
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},
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 1400x1000 with 4 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
|
|
},
|
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
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"text/plain": [
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"<Figure size 1000x500 with 1 Axes>"
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|
]
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
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}
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],
|
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"source": [
|
|
"import pandas as pd\n",
|
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"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
|
|
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"from sklearn.impute import SimpleImputer\n",
|
|
"from sklearn.metrics import (classification_report, confusion_matrix, \n",
|
|
" ConfusionMatrixDisplay, roc_curve, auc, \n",
|
|
" accuracy_score, f1_score)\n",
|
|
"\n",
|
|
"# Mengatur gaya plot agar rapi tanpa seaborn\n",
|
|
"plt.style.use('ggplot')\n",
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"\n",
|
|
"def main():\n",
|
|
" # --- 1. MEMUAT DATA ---\n",
|
|
" try:\n",
|
|
" df = pd.read_csv('Latest 2025 movies Datasets.csv')\n",
|
|
" print(f\"Dataset berhasil dimuat: {df.shape}\")\n",
|
|
" except FileNotFoundError:\n",
|
|
" print(\"Error: File csv tidak ditemukan. Pastikan nama file sesuai.\")\n",
|
|
" return\n",
|
|
"\n",
|
|
" # --- 2. PREPARASI DATA ---\n",
|
|
" # Target: Rating > 7.0 dianggap \"Layak Tonton\" (1)\n",
|
|
" df['is_watchable'] = (df['vote_average'] > 7.0).astype(int)\n",
|
|
" \n",
|
|
" # Fitur yang digunakan\n",
|
|
" X = df[['popularity', 'vote_count', 'original_language']]\n",
|
|
" y = df['is_watchable']\n",
|
|
"\n",
|
|
" # Split Data (80% Train, 20% Test)\n",
|
|
" # Stratify=y memastikan proporsi kelas seimbang di train dan test\n",
|
|
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
|
|
"\n",
|
|
" # --- 3. PIPELINE PREPROCESSING ---\n",
|
|
" # Pipeline ini akan menangani data kosong dan scaling secara otomatis\n",
|
|
" numeric_features = ['popularity', 'vote_count']\n",
|
|
" categorical_features = ['original_language']\n",
|
|
"\n",
|
|
" preprocessor = ColumnTransformer(\n",
|
|
" transformers=[\n",
|
|
" ('num', Pipeline([\n",
|
|
" ('imputer', SimpleImputer(strategy='median')),\n",
|
|
" ('scaler', StandardScaler()) # Scaling wajib untuk KNN\n",
|
|
" ]), numeric_features),\n",
|
|
" ('cat', Pipeline([\n",
|
|
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
|
|
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
|
|
" ]), categorical_features)\n",
|
|
" ])\n",
|
|
"\n",
|
|
" # --- 4. HYPERPARAMETER TUNING (Grid Search) ---\n",
|
|
" print(\"\\n\" + \"=\"*60)\n",
|
|
" print(\"PROSES TRAINING DAN TUNING SEDANG BERJALAN...\")\n",
|
|
" print(\"=\"*60)\n",
|
|
"\n",
|
|
" # --- A. TUNING KNN ---\n",
|
|
" print(\">> Melatih KNN (Mencari K terbaik)...\")\n",
|
|
" knn_pipeline = Pipeline([\n",
|
|
" ('preprocessor', preprocessor),\n",
|
|
" ('classifier', KNeighborsClassifier())\n",
|
|
" ])\n",
|
|
" \n",
|
|
" # Mencari kombinasi parameter terbaik\n",
|
|
" knn_params = {\n",
|
|
" 'classifier__n_neighbors': [3, 5, 7, 11],\n",
|
|
" 'classifier__weights': ['uniform', 'distance']\n",
|
|
" }\n",
|
|
" \n",
|
|
" grid_knn = GridSearchCV(knn_pipeline, knn_params, cv=3, scoring='f1', n_jobs=-1)\n",
|
|
" grid_knn.fit(X_train, y_train)\n",
|
|
" print(f\" KNN Terbaik: {grid_knn.best_params_}\")\n",
|
|
"\n",
|
|
" # --- B. TUNING RANDOM FOREST ---\n",
|
|
" print(\"\\n>> Melatih Random Forest...\")\n",
|
|
" rf_pipeline = Pipeline([\n",
|
|
" ('preprocessor', preprocessor),\n",
|
|
" ('classifier', RandomForestClassifier(random_state=42))\n",
|
|
" ])\n",
|
|
" \n",
|
|
" rf_params = {\n",
|
|
" 'classifier__n_estimators': [50, 100],\n",
|
|
" 'classifier__max_depth': [None, 10, 20],\n",
|
|
" 'classifier__min_samples_split': [2, 5]\n",
|
|
" }\n",
|
|
" \n",
|
|
" grid_rf = GridSearchCV(rf_pipeline, rf_params, cv=3, scoring='f1', n_jobs=-1)\n",
|
|
" grid_rf.fit(X_train, y_train)\n",
|
|
" print(f\" RF Terbaik: {grid_rf.best_params_}\")\n",
|
|
"\n",
|
|
" # --- 5. EVALUASI DAN VISUALISASI ---\n",
|
|
" \n",
|
|
" best_models = {\n",
|
|
" 'KNN': grid_knn.best_estimator_,\n",
|
|
" 'Random Forest': grid_rf.best_estimator_\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Canvas 1: Matriks Evaluasi (Confusion Matrix & ROC)\n",
|
|
" fig1, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
|
|
" fig1.suptitle('Evaluasi Detail: KNN vs Random Forest', fontsize=16)\n",
|
|
" plt.subplots_adjust(hspace=0.4, wspace=0.3)\n",
|
|
"\n",
|
|
" results_summary = {'Model': [], 'Accuracy': [], 'F1-Score': []}\n",
|
|
"\n",
|
|
" print(\"\\n\" + \"=\"*60)\n",
|
|
" print(\"HASIL EVALUASI AKHIR\")\n",
|
|
" print(\"=\"*60)\n",
|
|
"\n",
|
|
" for i, (name, model) in enumerate(best_models.items()):\n",
|
|
" # Prediksi ke data test\n",
|
|
" y_pred = model.predict(X_test)\n",
|
|
" \n",
|
|
" # Simpan skor untuk perbandingan nanti\n",
|
|
" acc = accuracy_score(y_test, y_pred)\n",
|
|
" f1 = f1_score(y_test, y_pred)\n",
|
|
" results_summary['Model'].append(name)\n",
|
|
" results_summary['Accuracy'].append(acc)\n",
|
|
" results_summary['F1-Score'].append(f1)\n",
|
|
"\n",
|
|
" # Print Laporan Teks\n",
|
|
" print(f\"\\nMODEL: {name}\")\n",
|
|
" print(\"-\" * 20)\n",
|
|
" print(classification_report(y_test, y_pred, target_names=['Lewati', 'Tonton']))\n",
|
|
" \n",
|
|
" # Plot 1: Confusion Matrix\n",
|
|
" cm = confusion_matrix(y_test, y_pred)\n",
|
|
" disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Lewati', 'Tonton'])\n",
|
|
" disp.plot(cmap='Blues', ax=axes[0, i], values_format='d', colorbar=False)\n",
|
|
" axes[0, i].set_title(f'Confusion Matrix - {name}')\n",
|
|
" axes[0, i].grid(False)\n",
|
|
"\n",
|
|
" # Plot 2: ROC Curve\n",
|
|
" y_prob = model.predict_proba(X_test)[:, 1]\n",
|
|
" fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
|
|
" roc_auc = auc(fpr, tpr)\n",
|
|
" \n",
|
|
" axes[1, i].plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')\n",
|
|
" axes[1, i].plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
|
|
" axes[1, i].set_xlim([0.0, 1.0])\n",
|
|
" axes[1, i].set_ylim([0.0, 1.05])\n",
|
|
" axes[1, i].set_xlabel('False Positive Rate')\n",
|
|
" axes[1, i].set_ylabel('True Positive Rate')\n",
|
|
" axes[1, i].set_title(f'ROC Curve - {name}')\n",
|
|
" axes[1, i].legend(loc=\"lower right\")\n",
|
|
"\n",
|
|
" # --- 6. PERBANDINGAN FINAL (BAR CHART) ---\n",
|
|
" # Ini memastikan KNN muncul berdampingan dengan RF di akhir\n",
|
|
" \n",
|
|
" fig2, ax2 = plt.subplots(figsize=(10, 6))\n",
|
|
" x = np.arange(len(results_summary['Model']))\n",
|
|
" width = 0.35\n",
|
|
" \n",
|
|
" rects1 = ax2.bar(x - width/2, results_summary['Accuracy'], width, label='Akurasi', color='#3498db')\n",
|
|
" rects2 = ax2.bar(x + width/2, results_summary['F1-Score'], width, label='F1-Score (Kelas Tonton)', color='#2ecc71')\n",
|
|
"\n",
|
|
" ax2.set_ylabel('Skor')\n",
|
|
" ax2.set_title('Perbandingan Performa Akhir: KNN vs Random Forest')\n",
|
|
" ax2.set_xticks(x)\n",
|
|
" ax2.set_xticklabels(results_summary['Model'])\n",
|
|
" ax2.set_ylim([0, 1.1])\n",
|
|
" ax2.legend()\n",
|
|
" \n",
|
|
" # Menambah label nilai di atas batang\n",
|
|
" def autolabel(rects):\n",
|
|
" for rect in rects:\n",
|
|
" height = rect.get_height()\n",
|
|
" ax2.annotate(f'{height:.2f}',\n",
|
|
" xy=(rect.get_x() + rect.get_width() / 2, height),\n",
|
|
" xytext=(0, 3), # 3 points vertical offset\n",
|
|
" textcoords=\"offset points\",\n",
|
|
" ha='center', va='bottom')\n",
|
|
"\n",
|
|
" autolabel(rects1)\n",
|
|
" autolabel(rects2)\n",
|
|
"\n",
|
|
" # --- 7. FEATURE IMPORTANCE (Hanya RF) ---\n",
|
|
" # Kita jelaskan kenapa hanya RF yang punya ini\n",
|
|
" print(\"\\n\" + \"=\"*60)\n",
|
|
" print(\"ANALISIS FITUR TERPENTING (Khusus Random Forest)\")\n",
|
|
" print(\"(Catatan: KNN tidak memiliki fitur ini karena berbasis jarak)\")\n",
|
|
" print(\"=\"*60)\n",
|
|
" \n",
|
|
" rf_model = grid_rf.best_estimator_.named_steps['classifier']\n",
|
|
" preprocessor_step = grid_rf.best_estimator_.named_steps['preprocessor']\n",
|
|
" ohe_features = preprocessor_step.named_transformers_['cat']['onehot'].get_feature_names_out(categorical_features)\n",
|
|
" all_features = numeric_features + list(ohe_features)\n",
|
|
" \n",
|
|
" importances = rf_model.feature_importances_\n",
|
|
" indices = np.argsort(importances)[::-1][:10]\n",
|
|
" \n",
|
|
" plt.figure(figsize=(10, 5))\n",
|
|
" plt.title(\"Top 10 Fitur Paling Berpengaruh (Random Forest Only)\")\n",
|
|
" plt.barh(range(len(indices)), importances[indices], color='#e74c3c', align='center')\n",
|
|
" plt.yticks(range(len(indices)), [all_features[i] for i in indices])\n",
|
|
" plt.xlabel('Importance')\n",
|
|
" plt.gca().invert_yaxis()\n",
|
|
" \n",
|
|
" print(\"Menampilkan semua grafik evaluasi...\")\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" main()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f541285b-b045-41e3-9550-800c7a192678",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|