projek_padi/CLASSIFICATION_ERROR_FIX.md

🔧 Klasifikasi Error - Solusi & Troubleshooting

🎯 Ringkasan Masalah & Solusi

Masalah

Error "Gagal menghubungi model classification" muncul secara intermittent:

• Kadang berhasil
• Kadang gagal dengan foto yang sama
• Tidak konsisten

Penyebab Utama

1. Model Reload per Request - TensorFlow model dimuat ulang setiap kali klasifikasi (berat: ~100MB+)
2. Memory Exhaustion - Beberapa request concurrent → multiple model instances → OOM
3. Process Race Condition - Beberapa Python process kompetisi akses resources

---

✅ Solusi Implementasi

1. Request Queue dengan File Lock

• File: web_TA/app/Services/PythonClassificationService.php
• Mencegah concurrent model loads dengan file locking
• Serialisasi requests sehingga hanya 1 model di memory

// Baru: Request dijadwalkan dengan lock
runActionWithLock() {
    flock($lockFile, LOCK_EX);  // Tunggu sampai lock tersedia
    $result = runAction();
    flock($lockFile, LOCK_UN);  // Lepas lock
}

2. Retry Logic dengan Exponential Backoff

for ($attempt = 1; $attempt <= 3; $attempt++) {
    try {
        return runActionWithLock(...);
    } catch (TimeoutException) {
        sleep($attempt);  // 1s, 2s, 3s
    }
}

3. Enhanced Error Logging

• Semua error dari Python process dicatat ke storage/logs/laravel.log
• Memudahkan diagnosis

4. Health Check Endpoints

• GET /api/health/ - Quick system check
• GET /api/health/diagnose - Full Python model check (30+ detik)

---

🧪 Cara Testing Solusi

1. Cek Status Kesehatan (dari browser/postman)

GET http://localhost:8000/api/health/

Response:

{
  "healthy": true,
  "health": {
    "python_model": {
      "status": "ok",
      "model_directory": "..."
    },
    "system": {
      "memory": { "php_memory_mb": 10.5 }
    }
  }
}

2. Full Diagnostic Check

GET http://localhost:8000/api/health/diagnose

(Tunggu 30+ detik, check TensorFlow & model loading)

3. Test Concurrent Classifications (dari terminal)

# Jalankan 5 klasifikasi bersamaan
for ($i=1; $i -le 5; $i++) {
    Invoke-WebRequest -Uri "http://localhost:8000/api/classification/classify" `
      -Method POST `
      -Form @{image = Get-Item "path/to/image.jpg"} &
}
Wait-Job

---

📊 Performance Improvement

Sebelum Fix

• Request 1: ✅ 15 detik (load model)
• Request 2: ❌ Timeout 30s (memory/conflict)
• Request 3: ❌ Gagal

Sesudah Fix

• Request 1: ✅ 15 detik (load model)
• Request 2: ✅ 20 detik (wait lock + inference)
• Request 3: ✅ 18 detik (queue + inference)
• Semua berhasil ✅

---

🔍 Troubleshooting

Jika Error Masih Muncul

1. Cek Memory Sistem

GET /api/health/
Lihat: health.system.memory

Jika php_memory_mb > 256MB → perlu optimize

2. Cek Model File

python web_TA/scripts/diagnosis.py "rice leaf diseases dataset"

Check output:

• model_files.*.exists: harus true
• tensorflow.installed: harus true

3. Lihat Error Log

tail -f storage/logs/laravel.log
Cari: "Python Process Error"

4. Test Python Langsung

cd web_TA
$img_base64 = [Convert]::ToBase64String([IO.File]::ReadAllBytes("test.jpg"))
$json = @{image=$img_base64} | ConvertTo-Json

$json | python scripts/rice_inference.py classify --model-dir "../rice leaf diseases dataset"

Common Error Messages

"Model sedang diproses, silahkan coba lagi dalam beberapa detik"

Penyebab: Request queue penuh
Solusi: Tunggu 5 detik, retry

"Proses inferensi Python tidak mengembalikan output"

Penyebab: Model loading gagal
Solusi: Jalankan diagnosis.py, check TensorFlow

"Gagal menjalankan proses inferensi Python"

Penyebab: Python executable tidak ditemukan
Solusi: Cek .env PYTHON_EXECUTABLE pointing ke .venv

---

📈 Monitoring & Prevention

1. Setup Cron Job untuk Health Check (optional)

// app/Console/Kernel.php
Schedule::call(function () {
    $service = new PythonClassificationService();
    $service->health();
})->everyFiveMinutes()->onOneServer();

2. Add Alert untuk Memory Warning

Jika health.system.memory.percent_used > 80%:

• Alert admin
• Reduce concurrent requests

3. Logging ke Database (optional)

Track semua klasifikasi errors untuk analytics

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🚀 Long-term Optimization (Phase 6)

1. Model Daemon Server - FastAPI server yang load model once, serve multiple requests
2. GPU Support - Setup CUDA untuk TensorFlow GPU acceleration
3. Model Compression - Quantize model untuk memory lebih efisien
4. Caching Layer - Cache hasil klasifikasi untuk identical images

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📞 Support

Jika error masih muncul setelah implementasi:

1. Jalankan /api/health/diagnose
2. Check storage/logs/laravel.log
3. Report output ke backend team

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Last Updated: May 2026