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{
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"text": [
"Sedang memproses data...\n",
"Sedang melatih model Random Forest (100 pohon)...\n",
"\n",
"========================================\n",
"HASIL EVALUASI MODEL\n",
"========================================\n",
"1. Single Split Test:\n",
" - R2 Score (Akurasi) : 0.8569 (85.69%)\n",
" - RMSE (Error Kuadrat): 0.9084\n",
" - MAE (Rata-rata Error): 0.5492 poin\n",
"\n",
"2. Cross Validation (5-Fold):\n",
" - Skor per tes : [0.54879999 0.77793875 0.82384791 0.84153289 0.8144776 ]\n",
" - Rata-rata R2 : 0.7613\n",
" - Kestabilan : +/- 0.1083\n",
"\n",
"========================================\n",
"FAKTOR PENENTU RATING (Feature Importance)\n",
"========================================\n",
" Fitur Kepentingan\n",
" vote_count 0.833263\n",
" popularity 0.066340\n",
" release_year 0.048906\n",
" release_month 0.027821\n",
"original_language_encoded 0.023670\n",
"\n",
"========================================\n",
"Contoh Prediksi: Rating Asli 7.0 | Prediksi Model 5.17\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
"\n",
"# ==========================================\n",
"# 1. LOAD DATA & PREPROCESSING\n",
"# ==========================================\n",
"print(\"Sedang memproses data...\")\n",
"df = pd.read_csv('Latest 2025 movies Datasets.csv')\n",
"\n",
"# Membersihkan nilai kosong (NaN)\n",
"df = df.dropna(subset=['release_date', 'vote_average', 'popularity', 'vote_count'])\n",
"df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')\n",
"df = df.dropna(subset=['release_date'])\n",
"\n",
"# Feature Engineering: Mengambil Tahun & Bulan\n",
"df['release_year'] = df['release_date'].dt.year\n",
"df['release_month'] = df['release_date'].dt.month\n",
"\n",
"# Mengubah Bahasa (teks) menjadi Angka\n",
"le = LabelEncoder()\n",
"df['original_language_encoded'] = le.fit_transform(df['original_language'])\n",
"\n",
"# Menentukan Fitur (X) dan Target (y)\n",
"features = ['popularity', 'vote_count', 'release_year', 'release_month', 'original_language_encoded']\n",
"X = df[features]\n",
"y = df['vote_average']\n",
"\n",
"# Membagi Data (80% Latih, 20% Uji)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# ==========================================\n",
"# 2. TRAINING MODEL (RANDOM FOREST)\n",
"# ==========================================\n",
"print(\"Sedang melatih model Random Forest (100 pohon)...\")\n",
"# Kita menggunakan algoritma JUARA kita\n",
"model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
"model.fit(X_train, y_train)\n",
"\n",
"# ==========================================\n",
"# 3. EVALUASI LENGKAP\n",
"# ==========================================\n",
"print(\"\\n\" + \"=\"*40)\n",
"print(\"HASIL EVALUASI MODEL\")\n",
"print(\"=\"*40)\n",
"\n",
"# Prediksi data test\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Menghitung Metrik\n",
"r2 = r2_score(y_test, y_pred)\n",
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
"mae = mean_absolute_error(y_test, y_pred)\n",
"\n",
"print(f\"1. Single Split Test:\")\n",
"print(f\" - R2 Score (Akurasi) : {r2:.4f} ({r2*100:.2f}%)\")\n",
"print(f\" - RMSE (Error Kuadrat): {rmse:.4f}\")\n",
"print(f\" - MAE (Rata-rata Error): {mae:.4f} poin\")\n",
"\n",
"# Cross Validation (5-Fold)\n",
"print(f\"\\n2. Cross Validation (5-Fold):\")\n",
"cv_scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n",
"print(f\" - Skor per tes : {cv_scores}\")\n",
"print(f\" - Rata-rata R2 : {cv_scores.mean():.4f}\")\n",
"print(f\" - Kestabilan : +/- {cv_scores.std():.4f}\")\n",
"\n",
"# ==========================================\n",
"# 4. FEATURE IMPORTANCE (RAHASIA MODEL)\n",
"# ==========================================\n",
"print(\"\\n\" + \"=\"*40)\n",
"print(\"FAKTOR PENENTU RATING (Feature Importance)\")\n",
"print(\"=\"*40)\n",
"\n",
"importances = model.feature_importances_\n",
"feature_importance_df = pd.DataFrame({'Fitur': features, 'Kepentingan': importances})\n",
"feature_importance_df = feature_importance_df.sort_values(by='Kepentingan', ascending=False)\n",
"\n",
"print(feature_importance_df.to_string(index=False))\n",
"\n",
"# Contoh Prediksi\n",
"print(\"\\n\" + \"=\"*40)\n",
"print(f\"Contoh Prediksi: Rating Asli {y_test.iloc[0]} | Prediksi Model {y_pred[0]:.2f}\")"
]
},
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