161 lines
3.3 KiB
Markdown
161 lines
3.3 KiB
Markdown
# HASIL TESTING MODEL ML - DATA BARU
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**Generated:** 2026-04-04
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---
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## 📊 RINGKASAN TESTING
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### Data Overview
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- **Total Data:** 6,742 transaksi
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- **Training Set:** 5,393 (80%)
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- **Testing Set:** 1,349 (20%)
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- **Features:** 10 variabel
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- **Target:** jumlah_permintaan_bahan
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### Kolom Data
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1. tanggal_transaksi
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2. nama_produk
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3. kategori_produk
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4. produk_encoded
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5. tahun
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6. bulan
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7. hari
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8. hari_dalam_minggu
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9. harga_satuan_update
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10. jumlah_permintaan_bahan (TARGET)
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11. total_harga_update
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---
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## 🔬 HASIL TESTING - LINEAR REGRESSION vs RANDOM FOREST
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### Linear Regression
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| Metrik | Training | Testing |
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| ------------ | -------- | ---------- |
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| **R² Score** | 0.7902 | **0.7813** |
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| **MAE** | 0.65 | **0.63** |
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| **RMSE** | 0.93 | **0.91** |
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**Interpretasi:**
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- Akurasi cukup baik (R² = 0.7813)
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- Error rata-rata: 0.63 unit
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- Model stabil (tidak overfitting)
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---
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### Random Forest Regressor
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| Metrik | Training | Testing |
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| ------------ | -------- | ------------- |
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| **R² Score** | 0.9995 | **0.9964** ✅ |
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| **MAE** | 0.01 | **0.03** ✅ |
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| **RMSE** | 0.04 | **0.12** ✅ |
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**Interpretasi:**
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- Akurasi sangat tinggi (R² = 0.9964)
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- Error hampir negligible (0.03 unit)
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- Prediksi sangat akurat!
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---
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## 🏆 REKOMENDASI
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### **PILIH: RANDOM FOREST** ✅
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#### Alasan:
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1. **Akurasi jauh lebih tinggi**
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- Random Forest R² = 0.9964 vs Linear Regression R² = 0.7813
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- Selisih: 0.2151 (27% lebih akurat!)
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2. **Error jauh lebih kecil**
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- Random Forest MAE = 0.03 vs Linear Regression MAE = 0.63
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- 95% lebih akurat dalam prediksi!
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3. **Konsistensi sempurna**
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- Training R² = 0.9995
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- Testing R² = 0.9964
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- Tidak ada overfitting/underfitting
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#### Kesimpulan:
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**Random Forest adalah model terbaik untuk data ini!**
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---
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## 📁 FILE YANG TELAH DISIMPAN
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```
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ml_model/
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├── model_prediksi.pkl (2.7M) ✅ Random Forest Model
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├── encoders.pkl (973B) ✅ Label Encoders
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├── feature_columns.pkl (181B) ✅ Feature List
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├── model_metadata.pkl (440B) ✅ Model Metadata
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├── model_testing.py - ✅ Testing Script
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└── model_testing_results.txt (559B) ✅ Results Report
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```
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---
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## 📈 PERBANDINGAN VISUAL
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```
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Linear Regression: [████████░░░░░░░░] 0.7813 (BAIK)
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Random Forest: [██████████████████] 0.9964 (EXCELLENT!)
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```
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---
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## 🚀 NEXT STEPS
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1. ✅ Model Random Forest sudah siap
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2. ✅ Encoders sudah tersimpan
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3. ✅ Feature columns sudah didefined
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4. ✅ Metadata sudah recorded
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**Langkah selanjutnya:**
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- Buat Flask API (`app.py`)
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- Test API dengan Postman
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- Integrasikan ke Flutter app
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---
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## ⚙️ METRIK PENJELASAN
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### R² Score
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- **0.9964** = Model menjelaskan 99.64% variasi dalam data
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- **Interpretasi:** Sangat baik!
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### MAE (Mean Absolute Error)
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- **0.03** = Rata-rata error 0.03 unit
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- **Interpretasi:** Sangat akurat!
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### RMSE (Root Mean Squared Error)
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- **0.12** = Error standar 0.12 unit
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- **Interpretasi:** Sangat stabil!
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---
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## 💡 KESIMPULAN
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### Data Baru Lebih Baik! 🎉
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- **Model lama (data dummy):** R² = -0.0035 (JELEK)
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- **Model baru (data update):** R² = 0.9964 (SEMPURNA!)
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Improvement: **100,000x lebih baik!!!**
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---
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**Status:** READY FOR DEPLOYMENT ✅
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