498 lines
21 KiB
Markdown
498 lines
21 KiB
Markdown
# AGENTS.md — Panduan AI untuk Sistem Deteksi PMK
|
||
|
||
File ini adalah **satu-satunya sumber kebenaran** untuk AI agent yang bekerja pada project ini. **WAJIB diperbarui** setiap kali ada perubahan arsitektur, dependensi, struktur database, atau alur sistem.
|
||
|
||
---
|
||
|
||
## 1. GAMBARAN PROYEK
|
||
|
||
**Sistem Deteksi dan Diagnosis Penyakit Mulut dan Kuku (PMK / FMD) pada Sapi**
|
||
|
||
- **Bahasa:** Python 3.12.5
|
||
- **Framework Web:** Flask (Bootstrap 5 UI)
|
||
- **Desktop (Legacy):** Tkinter (`main.py`, `predict_image.py`)
|
||
- **Deployment:** Railway (Nixpacks + gunicorn)
|
||
- **Repository:** `https://github.com/livindra/deteksi_PMK`
|
||
|
||
Dua jalur utama:
|
||
|
||
1. **Deteksi Berbasis Gambar** — ML (KNN) multi-class (sehat + tiap jenis PMK) + Computer Vision (color moments + GLCM texture)
|
||
2. **Sistem Pakar** — Forward chaining inference engine (29 gejala, 5 penyakit, 5 aturan)
|
||
|
||
> **Dataset:** Folder `dataset/` berisi `healthy/` dan folder `pmk_*` per jenis penyakit.
|
||
> `train_model.py` otomatis mendeteksi semua folder berawalan `pmk_*` sebagai kelas terpisah.
|
||
|
||
---
|
||
|
||
## 2. STRUKTUR PROYEK
|
||
|
||
```
|
||
deteksi_PMK/
|
||
├── app.py # Main Flask app (~1000 baris) — routing, prediksi, sistem pakar
|
||
├── expert_system.py # Forward chaining engine + KnowledgeBase + Evaluator
|
||
├── train_model.py # Training KNN model (k=5, euclidean, uniform)
|
||
├── setup_db.py # Inisialisasi database MySQL
|
||
├── main.py # LEGACY — Tkinter desktop menu
|
||
├── predict_image.py # LEGACY — Tkinter prediction GUI
|
||
├── test_forward_chaining.py # Unit test expert system
|
||
├── test_env.py # Test environment variables & DB connection
|
||
├── show_scaler_params.py # Utility: lihat parameter scaler
|
||
├── requirements.txt # Python dependencies
|
||
├── Procfile # gunicorn app:app
|
||
├── railway.json # Railway Nixpacks builder config
|
||
├── .env # Environment variables (TIDAK DI-COMMIT)
|
||
├── .env.example # Template env
|
||
├── .gitignore
|
||
├── .python-version # 3.12.5
|
||
│
|
||
├── QUICK_START.md # Panduan cepat (Bahasa Indonesia)
|
||
├── AGENTS.md # ← FILE INI — panduan AI, HARUS DIUPDATE
|
||
│
|
||
├── dataset/ # Dataset gambar training
|
||
│ ├── healthy/ # ~200 gambar sapi sehat
|
||
│ ├── pmk_oral/ # Gambar PMK oral
|
||
│ ├── pmk_podal/ # Gambar PMK podal (kaki)
|
||
│ ├── pmk_laktasi/ # Gambar PMK laktasi (ambing)
|
||
│ └── pmk_akut_general/ # Gambar PMK akut general
|
||
│
|
||
├── features/ # CSV fitur hasil ekstraksi
|
||
│ ├── dataset.csv
|
||
│ ├── data_train.csv
|
||
│ └── data_test.csv
|
||
│
|
||
├── models/ # Model terlatih (.pkl)
|
||
│ ├── knn_model.pkl # BINARY — KNeighborsClassifier (sehat/sakit)
|
||
│ ├── scaler.pkl # BINARY — StandardScaler
|
||
│ ├── label_encoder.pkl # BINARY — LabelEncoder
|
||
│ ├── multiclass_knn_model.pkl # MULTI — KNeighborsClassifier (jenis PMK)
|
||
│ ├── multiclass_scaler.pkl # MULTI — StandardScaler
|
||
│ ├── multiclass_label_encoder.pkl# MULTI — LabelEncoder
|
||
│ └── pca.pkl # TIDAK DIGUNAKAN (legacy)
|
||
│
|
||
├── uploads/ # Gambar hasil upload user
|
||
│ └── resize/ # Hasil resize
|
||
│ └── threshold/ # Hasil threshold
|
||
│
|
||
├── utils/
|
||
│ ├── __init__.py
|
||
│ ├── mysql_db.py # SQLAlchemy ORM — models, CRUD, seed data (916 baris)
|
||
│ ├── preprocessing.py # Validasi sapi + preprocessing pipeline (340 baris)
|
||
│ ├── feature_extraction.py # Ekstraksi fitur: RGB avg + HSV + GLCM + histogram + Hu (44 fitur)
|
||
│ └── helpers.py # Load/save model, dataset prep, confidence estimation (237 baris)
|
||
│
|
||
├── templates/ # Jinja2 templates
|
||
│ ├── base.html # Layout utama (Bootstrap 5, navbar, footer)
|
||
│ ├── index.html # Halaman utama
|
||
│ ├── upload.html # Form upload multi-file (JS sederhana)
|
||
│ ├── result.html # Hasil deteksi (sehat/sakit)
|
||
│ ├── riwayat_deteksi.html # Riwayat (client localStorage + MySQL)
|
||
│ ├── detail_deteksi.html # Detail satu prediksi + diagnosis
|
||
│ ├── expert_system.html # Sistem pakar — pilih gejala + hasil
|
||
│ └── diagnosis_history.html # Riwayat diagnosis sistem pakar
|
||
│
|
||
└── static/
|
||
├── css/style.css # Custom CSS (gradient, animasi, responsive)
|
||
└── js/main.js # JS — tooltips, file preview, API fetch, dark mode
|
||
```
|
||
|
||
---
|
||
|
||
## 3. ARSITEKTUR & ALUR DATA
|
||
|
||
### 3.1 Jalur Deteksi Gambar
|
||
|
||
```
|
||
User upload gambar
|
||
│
|
||
▼
|
||
[app.py] validate_cattle_image()
|
||
├─ Tolak wajah manusia (Haar cascade)
|
||
├─ Deteksi mata (HoughCircles) — bonus scoring
|
||
├─ Validasi warna HSV (coklat, merah, hitam, putih)
|
||
├─ Analisis tekstur (Laplacian variance)
|
||
├─ Edge detection (Canny)
|
||
└─ Final: weighted confidence (eye 15%, color 40%, texture 30%, edge 15%), threshold ≥ 65%
|
||
│
|
||
├─ Gagal → tolak dengan pesan error
|
||
│
|
||
▼
|
||
[preprocessing.py] preprocess_pipeline()
|
||
→ resize 128×128 → grayscale → Otsu threshold → mask → masked RGB + masked gray
|
||
│
|
||
▼
|
||
[feature_extraction.py] FeatureExtractor.extract_all_features()
|
||
→ 44 fitur: [RGB avg (3), HSV mean+std (6), GLCM (4), histogram 24, Hu (7)]
|
||
│
|
||
▼
|
||
[helpers.py] scaler.transform() → [BINARY MODEL] predict()
|
||
│
|
||
├ ─ 'sehat' → Simpan ke MySQL
|
||
│
|
||
▼ sakit
|
||
[helpers.py] multiclass_scaler.transform() → [MULTI-CLASS MODEL] predict()
|
||
→ label_encoder.inverse_transform()
|
||
│
|
||
▼
|
||
[helpers.py] estimate_prediction_confidence()
|
||
→ weighted neighbor confidence (clipped 50.0 – 98.5)
|
||
│
|
||
▼
|
||
Simpan ke MySQL (primary) + CSV (fallback)
|
||
│
|
||
▼
|
||
Semua gambar diproses — jika ada yang sakit:
|
||
→ Redirect ke expert-system?symptoms=G01,G02,...&mode=image
|
||
→ Gejala otomatis tercentang sesuai jenis PMK — hanya gejala yang relevan dengan body part PMK type tersebut (tidak cross-category)
|
||
Jika semua sehat:
|
||
→ Flash "Semua X gambar sehat" → /result/healthy
|
||
```
|
||
|
||
### 3.2 Jalur Sistem Pakar
|
||
|
||
```
|
||
User memilih gejala di web form
|
||
│
|
||
▼
|
||
[expert_system.py] ForwardChaining.tambah_gejala(gejala_list)
|
||
│
|
||
▼
|
||
ForwardChaining.inferensi()
|
||
→ Untuk setiap aturan:
|
||
- Hitung coverage = matched_count / total_count
|
||
- Hitung support_ratio = matched_rules / total_rules_for_disease
|
||
- Hitung evidence_strength = min(best_matched / 4, 1.0)
|
||
- Combined score = best_coverage*0.5 + avg_coverage*0.25 + support_ratio*0.15 + evidence_strength*0.1 + exact_bonus
|
||
- Threshold minimum: 0.35
|
||
│
|
||
▼
|
||
ForwardChaining.get_diagnosis()
|
||
→ Urutkan penyakit berdasarkan score
|
||
→ Kembalikan top diagnosis + matched rules + solusi
|
||
→ Severity map: P01=oral, P02=podal, P03=laktasi, P05=akut umum
|
||
│
|
||
▼
|
||
Simpan ke MySQL (tabel predictions + diagnosis_history)
|
||
│
|
||
▼
|
||
Render expert_system.html
|
||
```
|
||
|
||
### 3.3 Storage Hybrid
|
||
|
||
- **MySQL** (primary): tabel `predictions`, `diagnosis_history`, `expert_symptoms`, `expert_diseases`, `expert_rules`, join table `expert_rules_expert_symptoms`
|
||
- **localStorage** (browser): menyimpan array ID prediksi untuk ditampilkan di halaman riwayat
|
||
- **CSV fallback**: `results/predictions.csv`, `results/diagnosis_history.csv`
|
||
|
||
> **Batch upload:** Semua gambar dari satu sesi upload disimpan dalam **1 baris** tabel `predictions`. Data per-gambar (filename, prediction, pmk_type, confidence) disimpan sebagai JSON di kolom `images_data`. Field `prediction` diisi 'sakit' jika ada gambar sakit, 'sehat' jika semua sehat.
|
||
|
||
---
|
||
|
||
## 4. DATABASE (MySQL via SQLAlchemy)
|
||
|
||
### 4.1 Tabel
|
||
|
||
```sql
|
||
-- Tabel prediksi hasil deteksi gambar
|
||
predictions
|
||
id INT AUTO_INCREMENT PK
|
||
original_filename VARCHAR(255)
|
||
filename VARCHAR(255)
|
||
image_path VARCHAR(500)
|
||
prediction VARCHAR(50) -- 'sehat' / 'sakit'
|
||
confidence FLOAT
|
||
features TEXT -- JSON string dari extracted features
|
||
images_data TEXT -- JSON array dari per-gambar (batch upload)
|
||
timestamp DATETIME
|
||
|
||
-- Tabel riwayat diagnosis sistem pakar
|
||
diagnosis_history
|
||
id INT AUTO_INCREMENT PK
|
||
prediction_id INT FK → predictions.id (nullable)
|
||
diagnosis TEXT -- JSON string detail diagnosis
|
||
severity VARCHAR(50) -- 'ringan' / 'sedang' / 'berat'
|
||
timestamp DATETIME
|
||
|
||
-- Master gejala (29 gejala)
|
||
expert_symptoms
|
||
id INT AUTO_INCREMENT PK
|
||
code VARCHAR(10) UNIQUE -- G01–G29
|
||
description TEXT
|
||
category VARCHAR(50) -- umum, mulut, kaki, ambing, berat
|
||
display_order INT
|
||
|
||
-- Master penyakit (5 penyakit)
|
||
expert_diseases
|
||
id INT AUTO_INCREMENT PK
|
||
code VARCHAR(10) UNIQUE -- P01–P05
|
||
name VARCHAR(120)
|
||
description TEXT
|
||
solutions TEXT -- JSON array of strings
|
||
display_order INT
|
||
|
||
-- Aturan forward chaining (5 aturan)
|
||
expert_rules
|
||
id INT AUTO_INCREMENT PK
|
||
code VARCHAR(20) UNIQUE -- FC01–FC04
|
||
symptom_codes TEXT -- JSON array of symptom codes
|
||
result_disease_code VARCHAR(10) FK → expert_diseases.code
|
||
description TEXT
|
||
is_active BOOLEAN
|
||
display_order INT
|
||
|
||
-- Join table many-to-many
|
||
expert_rules_expert_symptoms
|
||
rule_id INT FK → expert_rules.id (ON DELETE CASCADE)
|
||
symptom_id INT FK → expert_symptoms.id (ON DELETE CASCADE)
|
||
```
|
||
|
||
### 4.2 Knowledge Base Default
|
||
|
||
**29 Gejala (G01–G29)** per kategori:
|
||
|
||
- **umum** (7): G01 demam >39.5°C, G11 nafsu makan turun, G12 lesu, G17 demam >40.5°C, G25 lebih sering berbaring, G28 takikardia, G29 sesak napas
|
||
- **mulut** (9): G02 air liur berlebih, G03 luka lidah/gusi, G04 nyeri setelah lepuh, G10 hidung berair, G14 lepuh moncong, G18 lepuh lidah meluas, G19 sulit mengunyah, G20 lepuh bantalan gigi, G21 bau mulut
|
||
- **kaki** (6): G05 lepuh celah kuku, G06 pincang, G15 nyeri kaki, G22 edema/radang, G23 bengkak celah kuku, G24 telapak kaki longgar
|
||
- **ambing** (5): G07 lepuh puting, G08 lesi puting, G09 produksi susu turun, G26 puting retak, G27 susu menggumpal
|
||
- **berat** (2): G13 miokarditis/kematian mendadak, G16 abortus/infertilitas
|
||
|
||
**5 Penyakit (P01–P05):**
|
||
|
||
- P01 = PMK_ORAL (gejala mulut)
|
||
- P02 = PMK_PODAL (gejala kaki/kuku)
|
||
- P03 = PMK_LAKTASI (gejala ambing)
|
||
- P05 = PMK_AKUT_GENERAL (gejala umum berat)
|
||
|
||
**4 Aturan (FC01–FC04):**
|
||
|
||
- FC01 → P01 (oral): [G01, G02, G03, G04, G11, G18, G19, G20, G21]
|
||
- FC02 → P02 (podal): [G01, G02, G05, G06, G15, G22, G23, G24, G25]
|
||
- FC03 → P03 (laktasi): [G01, G02, G07, G08, G09, G26, G27]
|
||
- FC04 → P05 (akut umum): [G01, G02, G03, G04, G05, G06, G07, G09, G11, G12, G14, G18, G20, G22, G23, G24, G26]
|
||
|
||
> **Auto-check gejala dari hasil deteksi gambar** (di `app.py:pmk_to_symptoms`):
|
||
> Mapping ini digunakan saat redirect ke expert system via mode=image. Hanya gejala spesifik body part yang diikutkan (tanpa gejala umum):
|
||
> - `pmk_oral` → gejala mulut: [G02, G03, G04, G10, G14, G18, G19, G20, G21]
|
||
> - `pmk_podal` → gejala kaki: [G05, G06, G15, G22, G23, G24]
|
||
|
||
> **Jika menambah/mengubah gejala, penyakit, atau aturan**, update data di:
|
||
>
|
||
> 1. `utils/mysql_db.py` — constants `DEFAULT_EXPERT_SYMPTOMS`, `DEFAULT_EXPERT_DISEASES`, `DEFAULT_EXPERT_RULES`
|
||
> 2. File ini (AGENTS.md) — bagian ini
|
||
> 3. Jalankan ulang `seed_expert_knowledge(force=True)` atau hapus tabel agar di-reseed
|
||
|
||
---
|
||
|
||
## 5. API ENDPOINTS (Flask)
|
||
|
||
| Method | Route | Deskripsi |
|
||
| -------- | -------------------------------- | -------------------------------------------------- |
|
||
| GET | `/` | Homepage — model status + recent history |
|
||
| GET | `/upload` | Halaman upload gambar |
|
||
| POST | `/predict` | Upload + prediksi gambar multi-file. Semua gambar disimpan sbg 1 baris di DB. Jika sakit → redirect ke expert-system dgn gejala otomatis + semua gambar ditampilkan. Jika sehat → /result/healthy |
|
||
| GET | `/result/healthy` | Hasil sehat |
|
||
| GET | `/result/sick` | Hasil sakit (legacy, tidak dipakai di alur baru) |
|
||
| GET | `/riwayat_deteksi` | Halaman riwayat deteksi |
|
||
| GET | `/detail-deteksi/<int:pred_id>` | Detail prediksi + diagnosis |
|
||
| POST | `/api/validate-image` | Validasi gambar real-time (apakah sapi) |
|
||
| POST | `/api/get_data_riwayat_deteksi` | Ambil riwayat berdasarkan localStorage IDs |
|
||
| POST | `/api/diagnosis` | API diagnosis sistem pakar |
|
||
| GET/POST | `/expert-system` | Halaman sistem pakar (GET=form dgn param `symptoms` untuk pre-check, POST=diagnosis) |
|
||
| GET | `/expert-system/from-prediction` | Redirect ke expert system mode gambar |
|
||
| GET | `/riwayat-diagnosis` | Halaman riwayat diagnosis |
|
||
| GET | `/export/csv` | Export prediksi ke CSV |
|
||
| GET | `/export/excel` | Export prediksi ke Excel |
|
||
| GET | `/clear-session` | Bersihkan Flask session |
|
||
|
||
---
|
||
|
||
## 6. ML MODEL DETAILS
|
||
|
||
**Arsitektur Hierarchical (2-level):**
|
||
|
||
```
|
||
Input image → 44 fitur (RGB+HSV+GLCM+Histogram+Hu)
|
||
│
|
||
┌──────┴──────┐
|
||
▼ ▼
|
||
[BINARY MODEL] [selamat — end]
|
||
sehat vs sakit
|
||
│ (sakit)
|
||
▼
|
||
[MULTI-CLASS MODEL]
|
||
pmk_oral / pmk_podal / pmk_laktasi / pmk_akut_general
|
||
```
|
||
|
||
### Binary Model (prefix='')
|
||
|
||
| Parameter | Value |
|
||
| ----------------- | -------------------------------- |
|
||
| **Task** | sehat vs sakit |
|
||
| **Algorithm** | K-Nearest Neighbors (KNN) |
|
||
| **k** | Hyperparameter tuned (1–15) |
|
||
| **Distance** | Hyperparameter tuned (euclidean/manhattan) |
|
||
| **Weight** | Hyperparameter tuned (uniform/distance) |
|
||
| **Accuracy** | ±98% |
|
||
| **Model file** | `models/knn_model.pkl` |
|
||
|
||
### Multi-class Model (prefix='multiclass_')
|
||
|
||
| Parameter | Value |
|
||
| ----------------- | -------------------------------- |
|
||
| **Task** | Jenis PMK (hanya data sakit) |
|
||
| **Algorithm** | K-Nearest Neighbors (KNN) |
|
||
| **k** | Hyperparameter tuned (1–15) |
|
||
| **Distance** | Hyperparameter tuned (euclidean/manhattan) |
|
||
| **Weight** | Hyperparameter tuned (uniform/distance) |
|
||
| **Accuracy** | ±60% |
|
||
| **Model file** | `models/multiclass_knn_model.pkl`|
|
||
|
||
### Common
|
||
|
||
| Parameter | Value |
|
||
| ----------------- | --------------------------------------------------------------------------- |
|
||
| **Features** | 44: avg_red, avg_green, avg_blue, hsv_h_mean, hsv_h_std, hsv_s_mean, hsv_s_std, hsv_v_mean, hsv_v_std, contrast, homogeneity, correlation, energy, 8-bin histogram × 3 channels (24), hu_moment_0..6 (7) |
|
||
| **Image size** | 256×256 |
|
||
| **Preprocessing** | Otsu threshold, mask, resize |
|
||
| **Scaling** | StandardScaler |
|
||
| **Output** | Binary: `sehat` / `sakit`; Multi-class: `pmk_oral`, `pmk_podal`, `pmk_laktasi`, `pmk_akut_general`, ... |
|
||
| **Confidence** | Custom weighted neighbor, clipped 50.0–89.5% |
|
||
| **Class detection** | Auto-detect all folders under `dataset/` — `healthy` → `sehat`, `pmk_*` → nama folder |
|
||
|
||
> **Jika menambah/mengubah fitur**, update:
|
||
>
|
||
> 1. `utils/feature_extraction.py` — `FeatureExtractor.feature_names`
|
||
> 2. `train_model.py` — feature export dimensions
|
||
> 3. Retrain model (`python train_model.py`)
|
||
> 4. AGENTS.md ini
|
||
>
|
||
> **Jika menambah kelas penyakit baru** (folder `pmk_*`):
|
||
>
|
||
> 1. Buat folder `dataset/pmk_nama_baru/` — otomatis terdeteksi oleh `prepare_dataset()`
|
||
> 2. Retrain model (`python train_model.py`)
|
||
> 3. Update AGENTS.md
|
||
|
||
---
|
||
|
||
## 7. CATTLE IMAGE VALIDATION
|
||
|
||
Lokasi: `utils/preprocessing.py:validate_cattle_image()`
|
||
|
||
Layer validasi berlapis:
|
||
|
||
1. **Face rejection** — Haar cascade face detection (tolak foto manusia)
|
||
2. **Eye detection** — HoughCircles (bonus scoring)
|
||
3. **Color validation** — HSV threshold (brown, red, black, white)
|
||
4. **Texture analysis** — Laplacian variance (range 45–3500)
|
||
5. **Edge detection** — Canny edge density (range 0.01–0.35)
|
||
|
||
Final confidence = `eye_score*0.15 + color_score*0.40 + texture_score*0.30 + edge_score*0.15`
|
||
Threshold: **≥ 65%** untuk lolos sebagai sapi.
|
||
|
||
---
|
||
|
||
## 8. DEPLOYMENT
|
||
|
||
### Railway
|
||
|
||
- **Builder:** Nixpacks (`railway.json`)
|
||
- **Start:** `gunicorn app:app --bind 0.0.0.0:$PORT`
|
||
- **Env:** `FLASK_SECRET`, `DATABASE_URL` atau `MYSQLHOST/PORT/USER/PASSWORD/DATABASE`
|
||
- `opencv-python-headless` — tidak butuh GUI library
|
||
|
||
### Local
|
||
|
||
```bash
|
||
# 1. Setup database
|
||
python setup_db.py
|
||
|
||
# 2. (Opsional) Training model
|
||
python train_model.py
|
||
|
||
# 3. Jalankan app
|
||
python app.py
|
||
# Buka http://localhost:5000
|
||
```
|
||
|
||
---
|
||
|
||
## 9. TESTING
|
||
|
||
```bash
|
||
# Test environment & database
|
||
python test_env.py
|
||
|
||
# Test expert system forward chaining
|
||
python test_forward_chaining.py
|
||
```
|
||
|
||
> **Jika menambah fitur baru**, tambahkan test case di file terkait. Jika menambah test file baru, update bagian ini.
|
||
|
||
---
|
||
|
||
## 10. CONFIGURATION
|
||
|
||
### Environment Variables (`.env`)
|
||
|
||
```
|
||
DB_HOST=localhost
|
||
DB_PORT=3306
|
||
DB_USER=root
|
||
DB_PASSWORD=
|
||
DB_NAME=deteksi_pmk
|
||
MYSQLHOST= # Railway
|
||
MYSQLPORT= # Railway
|
||
MYSQLUSER= # Railway
|
||
MYSQLPASSWORD= # Railway
|
||
MYSQLDATABASE= # Railway
|
||
DATABASE_URL= # Railway
|
||
FLASK_SECRET=deteksi-pmk-secret-key-2026
|
||
FLASK_DEBUG=0
|
||
```
|
||
|
||
---
|
||
|
||
## 11. ATURAN UNTUK AI AGENT
|
||
|
||
1. **Jangan pernah mengubah fungsionalitas tanpa memperbarui file ini.**
|
||
2. **Jika menambah variabel environment**, tambahkan ke `.env.example` dan dokumentasikan di sini.
|
||
3. **Jika mengubah struktur tabel**, update: (a) model SQLAlchemy di `utils/mysql_db.py`, (b) skema di AGENTS.md, (c) constants `DEFAULT_EXPERT_*`.
|
||
4. **Jika menambah endpoint**, dokumentasikan di bagian API ENDPOINTS.
|
||
5. **Jika mengubah fitur ML**, update `feature_names`, retrain model, update AGENTS.md.
|
||
6. **Jika menambah dependensi**, tambahkan ke `requirements.txt` dan update tabel dependensi.
|
||
7. **File ini WAJIB dibaca** sebelum melakukan perubahan signifikan.
|
||
8. **Gunakan Bahasa Indonesia** untuk komentar, UI, dan dokumentasi end-user karena target pengguna adalah peternak/inspektur Indonesia.
|
||
|
||
---
|
||
|
||
## 12. DEPENDENSI (requirements.txt)
|
||
|
||
| Package | Version | Kegunaan |
|
||
| ---------------------- | -------- | --------------------------------- |
|
||
| opencv-python-headless | 4.9.0.80 | Image processing, validasi sapi |
|
||
| numpy | <2 | Numerical operations |
|
||
| pandas | ≥2.1.0 | Data manipulation |
|
||
| scikit-learn | ≥1.3.2 | KNN, StandardScaler, LabelEncoder |
|
||
| scikit-image | ≥0.22.0 | GLCM texture features |
|
||
| matplotlib | ≥3.8.0 | Feature analysis plots |
|
||
| pillow | ≥10.1.0 | Image loading fallback |
|
||
| joblib | ≥1.3.2 | Model serialization |
|
||
| Flask | ≥2.3.3 | Web framework |
|
||
| SQLAlchemy | ≥2.0.0 | ORM MySQL |
|
||
| PyMySQL | ≥1.1.0 | MySQL driver |
|
||
| python-dotenv | ≥1.0.0 | Environment loading |
|
||
| gunicorn | ≥21.2.0 | WSGI production server |
|
||
| pytz | ≥2024.1 | Timezone (Asia/Jakarta) |
|
||
| cryptography | ≥3.4.8 | MySQL password encryption |
|
||
|
||
---
|
||
|
||
## 13. LEGACY CODE (TIDAK DIGUNAKAN LAGI)
|
||
|
||
File berikut adalah kode legacy desktop (Tkinter) yang **tidak aktif** di web app:
|
||
|
||
- `main.py` — Menu utama desktop
|
||
- `predict_image.py` — Prediksi gambar via GUI Tkinter
|
||
- `models/pca.pkl` — PCA model tidak digunakan (legacy)
|
||
|
||
> Jika ada refactoring besar, pertimbangkan untuk menghapus file-file ini atau tandai secara eksplisit.
|