| # 🔧 Tools Directory |
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| Folder ini berisi model machine learning dan konfigurasi untuk anomali detection. |
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| ## 📦 Files Required |
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| ``` |
| tools/ |
| ├── scaler.pkl ← StandardScaler model (binary) |
| ├── isolation_forest.pkl ← Isolation Forest model (binary) |
| ├── metadata.json ← Configuration |
| ├── model_train.py ← Training script (optional) |
| └── simpanan_analisis.py ← Helper functions (optional) |
| ``` |
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| --- |
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| ## 📋 File Descriptions |
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| ### scaler.pkl |
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| - **Type:** Binary (Pickle format) |
| - **Purpose:** Standardize features untuk ML model |
| - **Used by:** Tab 2 (Analisa Simpanan) |
| - **Size:** ~1-2 KB |
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| ### isolation_forest.pkl |
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| - **Type:** Binary (Pickle format) |
| - **Purpose:** Pre-trained Isolation Forest model untuk anomali detection |
| - **Used by:** Tab 2 (Analisa Simpanan) |
| - **Features:** 8 fitur (Db_Sukarela dan Cr_Sukarela aggregates) |
| - **Size:** ~10-50 KB |
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| ### metadata.json |
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| - **Type:** JSON (Text) |
| - **Purpose:** Konfigurasi model dan parameter |
| - **Content:** |
| ```json |
| { |
| "feature_cols": [...], // Nama kolom untuk model |
| "rolling_zscore_threshold": 1.0, // Threshold untuk Z-Score |
| "rolling_window": 3 // Window size untuk rolling aggregation |
| } |
| ``` |
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| ### model_train.py (Optional) |
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| Script untuk melatih ulang model jika diperlukan. |
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| ### simpanan_analisis.py (Optional) |
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| Helper functions untuk analisa simpanan. |
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| --- |
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| ## ⚠️ Important Notes |
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| ### Binary Model Files (.pkl) |
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| - File `scaler.pkl` dan `isolation_forest.pkl` HARUS ada untuk Tab 2 berfungsi |
| - Files ini di-generate saat training, bukan di-create manual |
| - Jangan di-edit manual - format binary |
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| ### File Locations |
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| - App mencari file di path relatif: `../tools/` (dari app folder) |
| - Pastikan struktur folder sesuai template |
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| ### Model Updates |
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| Jika ingin update model (retrain): |
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| 1. Jalankan `python tools/model_train.py` |
| 2. File `.pkl` baru akan di-generate |
| 3. Restart aplikasi |
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| --- |
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| ## 🔍 Troubleshooting |
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| ### Error: "Model tidak ditemukan" |
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| ``` |
| Solusi: |
| 1. Pastikan scaler.pkl ada di tools/ |
| 2. Pastikan isolation_forest.pkl ada di tools/ |
| 3. Check file paths (case-sensitive di Linux/Mac) |
| ``` |
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| ### Error: "Model load failed" |
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| ``` |
| Solusi: |
| 1. Pastikan Python version sama saat training dan running |
| 2. Pastikan scikit-learn version sama |
| 3. Re-train model jika masih error |
| ``` |
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| --- |
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| ## 📊 Model Info |
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| ### Isolation Forest |
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| - **Algorithm:** Anomaly detection via Isolation Forest |
| - **Training data:** Historical simpanan transactions |
| - **Features:** 8 aggregated metrics |
| - Db_Sukarela_Total, Db_Sukarela_Avg, Db_Sukarela_Std, Db_Sukarela_Max |
| - Cr_Sukarela_Total, Cr_Sukarela_Avg, Cr_Sukarela_Std, Cr_Sukarela_Max |
| - **Output:** Binary (Anomaly: -1, Normal: 1) |
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| ### StandardScaler |
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| - **Purpose:** Feature normalization |
| - **Fitted on:** Historical data statistics |
| - **Usage:** Pre-process features before model prediction |
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| --- |
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| ## 🚀 Quick Check |
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| Verify tools setup: |
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| ```bash |
| cd THC_APP |
| python -c "import joblib; print('scaler:', joblib.load('tools/scaler.pkl')); print('model:', joblib.load('tools/isolation_forest.pkl'))" |
| ``` |
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| Success jika output menunjukkan objects tanpa error. |
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| --- |
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| **Last Updated:** May 2026 |
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