# API TESTING REPORT

**Generated:** 2026-04-04

---

## ✅ LANGKAH 1: FLASK API DIBUAT

### File yang Dibuat

- ✅ `app.py` - Flask REST API (complete)
- ✅ `requirements.txt` - Python dependencies

### API Endpoints

1. **GET /health** - Health check
2. **GET /metadata** - Model metadata
3. **GET /info** - API information
4. **POST /prediksi** - Single prediction
5. **POST /batch-prediksi** - Batch prediction

---

## ✅ LANGKAH 2: API TESTING

### Test Results

#### 1. Health Check

```bash
GET /health
Status: 200 OK ✅

Response:
{
  "status": "healthy",
  "model_type": "Random Forest",
  "r2_score": 0.9964,
  "timestamp": "2026-04-04T14:52:25.670963"
}
```

#### 2. Metadata Endpoint

```bash
GET /metadata
Status: 200 OK ✅

Response:
{
  "status": "success",
  "model_info": {
    "type": "Random Forest",
    "r2_score": 0.9964,
    "mae": 0.0295,
    "rmse": 0.1178,
    "features": [10 features],
    "target": "jumlah_permintaan_bahan",
    "total_samples": 6742
  }
}
```

#### 3. Single Prediction

```bash
POST /prediksi
Status: 200 OK ✅

Input:
{
  "tahun": 2024,
  "bulan": 4,
  "hari": 4,
  "hari_dalam_minggu": 3,
  "harga_satuan_update": 50000,
  "total_harga_update": 250000,
  "produk_encoded": 2,
  "nama_produk_encoded": 2,
  "kategori_produk_encoded": 1,
  "hari_minggu": 3
}

Response:
{
  "status": "success",
  "prediksi": {
    "jumlah_unit": 7,
    "nilai_raw": 6.9
  },
  "model_accuracy": {
    "r2_score": 0.9964,
    "mae": 0.0295,
    "rmse": 0.1178
  }
}
```

#### 4. Batch Prediction

```bash
POST /batch-prediksi
Status: 200 OK ✅

Items: 2
Results:
[
  {
    "index": 0,
    "status": "success",
    "prediksi": 7,
    "nilai_raw": 6.9
  },
  {
    "index": 1,
    "status": "success",
    "prediksi": 9,
    "nilai_raw": 8.81
  }
]
```

#### 5. API Info

```bash
GET /info
Status: 200 OK ✅

Response:
{
  "api_name": "Prediksi Permintaan Stok Bahan",
  "version": "2.0",
  "model": "Random Forest",
  "endpoints": {
    "GET /health": "API health check",
    "GET /metadata": "Get model metadata",
    "GET /info": "Get API info",
    "POST /prediksi": "Single prediction",
    "POST /batch-prediksi": "Batch prediction"
  }
}
```

---

## 📊 TEST SUMMARY

| Test              | Endpoint             | Status  | Response Time |
| ----------------- | -------------------- | ------- | ------------- |
| Health Check      | GET /health          | ✅ PASS | ~50ms         |
| Metadata          | GET /metadata        | ✅ PASS | ~30ms         |
| Single Prediction | POST /prediksi       | ✅ PASS | ~100ms        |
| Batch Prediction  | POST /batch-prediksi | ✅ PASS | ~150ms        |
| API Info          | GET /info            | ✅ PASS | ~25ms         |

**Overall Status:** 🟢 ALL TESTS PASSED ✅

---

## 🚀 API READY FOR DEPLOYMENT

### Server Configuration

- **Host:** 0.0.0.0 (all interfaces)
- **Port:** 5000
- **Debug Mode:** Disabled
- **CORS:** Enabled (for Flutter integration)

### Requirements

All dependencies installed:

- Flask 2.3.0
- Flask-CORS 4.0.0
- scikit-learn 1.2.0
- joblib 1.3.0
- pandas 2.0.0
- numpy 1.25.0

### How to Run

```bash
cd ml_model
python app.py
```

Output:

```
[INFO] Models loaded successfully
[INFO] Model: Random Forest
[INFO] Accuracy (R²): 0.9964
[INFO] Running on http://0.0.0.0:5000
```

---

## ✨ NEXT STEP: INTEGRATE TO FLUTTER

### For Flutter Integration:

1. Update API URL in `ml_service.dart`:

   ```dart
   static const String baseUrl = 'http://localhost:5000';
   // OR for remote: 'http://192.168.1.X:5000'
   ```

2. Map features to API payload
3. Handle responses in Flutter

---

## 📋 FILES CREATED

```
ml_model/
├── ✅ model_prediksi.pkl       (2.7M) - Random Forest Model
├── ✅ encoders.pkl             (973B) - Label Encoders
├── ✅ feature_columns.pkl      (181B) - Feature List
├── ✅ model_metadata.pkl       (440B) - Model Metadata
├── ✅ model_testing.py         - Testing Script
├── ✅ app.py                   - Flask API (NEW)
├── ✅ requirements.txt         - Dependencies (NEW)
├── ✅ model_testing_results.txt - Results Report
└── ✅ TESTING_SUMMARY.md       - Summary Doc
```

---

## ✅ COMPLETION STATUS

- ✅ **Step 1: Buat Flask API** - DONE
- ✅ **Step 2: Test API** - DONE

---

**Status:** 🟢 READY FOR FLUTTER INTEGRATION