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PANDUAN APLIKASI EAGLEARN

Focus Monitoring System - User Guide

Versi: 1.0 Last Updated: 8 Januari 2026 Application: EAGLEARN Focus Monitoring

📋 DAFTAR ISI

  1. Tentang Aplikasi
  2. Persyaratan Sistem
  3. Instalasi
  4. Menjalankan Aplikasi
  5. Panduan Penggunaan
  6. Fitur-Fitur
  7. Konfigurasi
  8. Troubleshooting
  9. FAQ

🎯 TENTANG APLIKASI

EAGLEARN adalah aplikasi focus monitoring berbasis AI yang mendeteksi:

  • Emotion - 7 jenis emosi (happy, sad, angry, surprised, neutral, fear, disgust)
  • Head Pose - Posisi kepala (pitch, yaw, roll)
  • Eye Tracking - Arah pandangan mata
  • Body Posture - Postur tubuh dan fokus
  • Blink Rate - Frekuensi kedipan mata
  • Yawning Detection - Deteksi mengantuk
  • Stress & Confusion - Level stress dan kebingungan

Teknologi:

  • MediaPipe untuk face & pose detection
  • DeepFace untuk emotion recognition
  • Flask + SocketIO untuk real-time communication
  • PyTorch & TensorFlow untuk model AI

💻 PERSYARATAN SISTEM

Minimum Requirements:

  • OS: Windows 10/11, Linux (Ubuntu 20.04+), macOS 10.15+
  • Python: 3.9 - 3.11 (recommended: 3.11.9)
  • RAM: 8 GB (16 GB recommended)
  • Processor: Intel Core i5 / AMD Ryzen 5 ke atas
  • Storage: 5 GB free space
  • Webcam: Built-in atau external (640x480 minimum)
  • Browser: Chrome, Firefox, Edge (WebSocket support)

Recommended (untuk GPU acceleration):

  • GPU: NVIDIA GTX 1650 ke atas (RTX 3050+ recommended)
  • VRAM: 4 GB minimum
  • CUDA: 11.8 atau 12.x

🚀 INSTALASI

Step 1: Clone Repository

git clone <repository-url>
cd Eaglearn-Project

Step 2: Buat Virtual Environment

# Buat virtual environment
python -m venv .venv_gpu

# Activate virtual environment
# Windows:
.venv_gpu\Scripts\activate

# Linux/macOS:
source .venv_gpu/bin/activate

Step 3: Install Dependencies

# Upgrade pip
python -m pip install --upgrade pip

# Install semua dependencies
pip install -r requirements.txt

# Install PyTorch dengan CUDA (jika ada GPU)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Step 4: Verifikasi Instalasi

# Cek Python
python --version  # Expected: 3.9-3.11

# Cek PyTorch GPU (jika ada GPU)
python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
# Expected: CUDA available: True

# Cek MediaPipe
python -c "import mediapipe as mp; print('MediaPipe:', mp.__version__)"
# Expected: MediaPipe: 0.10.8

▶️ MENJALANKAN APLIKASI

Opsi 1: Run secara Manual

# Activate virtual environment
source .venv_gpu/Scripts/activate  # Windows
source .venv_gpu/bin/activate     # Linux/macOS

# Jalankan aplikasi
python app.py

Aplikasi akan berjalan di:

Opsi 2: Run dengan Batch File (Windows)

Buat file run_eaglearn.bat:

@echo off
call .venv_gpu\Scripts\activate
python app.py
pause

Klik dua kali file tersebut untuk menjalankan aplikasi.

Opsi 3: Run sebagai Background Service (Linux/macOS)

# Jalankan di background
nohup python app.py > eaglearn.log 2>&1 &

# Cek logs
tail -f eaglearn.log

📖 PANDUAN PENGGUNAAN

1. Buka Aplikasi di Browser

  1. Buka browser (Chrome/Firefox/Edge)
  2. Navigate ke: http://localhost:8080
  3. Allow camera access ketika diminta

2. Mulai Monitoring Session

Tampilan Utama:

┌─────────────────────────────────────────────────────────┐
│                    EAGLEARN                             │
│                                                          │
│  [Start Session]  [Stop Session]  [Calibrate]         │
│                                                          │
│  ┌─────────────────────────────────────────────────────┐│
│  │            CAMERA FEED                                ││
│  │                                                       ││
│  │              [Your face will appear here]            ││
│  │                                                       ││
│  └─────────────────────────────────────────────────────┘│
│                                                          │
│  Status: FOCUSED                                          │
│  Focus Score: 85%                                        │
│  Emotion: Happy (95%)                                     │
│  FPS: 27.5                                                │
└─────────────────────────────────────────────────────────┘

3. Interpretasi Hasil

Focus Score:

  • 85-100% 🟢 = Focused
  • 50-84% 🟡 = Need Attention
  • 0-49% 🔴 = Distracted/Drowsy

Emotion Indicators:

  • 😊 Happy = Engagement good
  • 😐 Neutral = Normal state
  • 😠 Angry = Frustration/struggle
  • 😨 Fear/Surprise = Confusion/uncertainty
  • 😢 Sad = Lack of motivation

Additional Metrics:

  • Head Pose = Posisi kepala (harus menghadap depan)
  • Eye Gaze = Arah pandangan (harus ke screen)
  • Posture = Postur tubuh (harus tegak)
  • Blink Rate = Kedipan normal (12-25 per minute)
  • Yawning = Deteksi mengantuk (tanda fatigue)

4. Calibration

Untuk akurasi terbaik, lakukan calibration:

  1. Klik tombol [Calibrate]
  2. Ikuti instruksi:
    • Dudur tegak di depan kamera
    • Pandang lurus ke screen
    • Posisi wajah terlihat jelas
    • Tahan 5-10 detik
  3. Klik [Save Calibration]

Calibration data akan disimpan di user_calibration.json

🎛️ FITUR-FITUR

Real-time Monitoring

  • Live Video Feed - Webcam stream dengan overlay
  • Face Detection - Bounding box dan landmarks
  • Emotion Detection - Update real-time (5-10 FPS)
  • Focus Tracking - Score focus 0-100%
  • Performance Metrics - FPS, frame skip, processing time

Session Management

  • Start/Stop - Kontrol monitoring session
  • Auto-save - Session data disimpan otomatis
  • History - Review session sebelumnya
  • Export - Download session data (CSV/JSON)

Advanced Features

  • Gaze Estimation - Prediksi arah pandangan (x, y coordinate)
  • Attention Score - 0-100, seberapa fokus ke screen
  • Distraction Alerts - Notifikasi jika loss of focus
  • Fatigue Detection - Yawning dan drowsiness
  • Stress Analysis - Level stress dari micro-expressions

⚙️ KONFIGURASI

File: config.yaml

Camera Settings:

camera:
  width: 640           # Resolution lebar
  height: 480          # Resolution tinggi
  fps: 30              # Target FPS
  backend: dshow       # Windows: dshow, Linux: v4l2

Performance Tuning:

performance:
  frame_skip_mode: adaptive
  frame_skip_base: 3   # Process setiap N frame
  adaptive_quality:
    enabled: true
    target_fps: 25     # FPS target
    min_skip: 1
    max_skip: 7
  gpu_acceleration:
    enabled: true
    fallback_to_cpu: true

Emotion Detection:

emotion:
  min_confidence: 0.3    # Threshold confidence
  yawning_mar_threshold: 0.6
  blink_ear_threshold: 0.21
  stress_high_threshold: 0.7

Environment Variables

Buat file .env:

# Camera
CAMERA_ID=0
CAMERA_WIDTH=640
CAMERA_HEIGHT=480

# Server
HOST=127.0.0.1
PORT=8080
DEBUG=False

# Privacy
RETENTION_DAYS=30

🐛 TROUBLESHOOTING

Problem: Camera Not Detected

Symptoms:

  • Error: "Cannot open webcam"
  • Black screen
  • "No camera found"

Solutions:

  1. Check camera connection:

    # Windows
python -c "import cv2; cap = cv2.VideoCapture(0); print('Open:', cap.isOpened())"

  2. Try different backends:

    • Edit config.yaml: backend: default
    • Atau gunakan camera ID lain: CAMERA_ID=1

  3. Close other apps:

    • Close Zoom, Teams, Skype, etc.
    • Only one app can access camera at a time

Problem: Low FPS (< 15)

Symptoms:

  • Choppy video
  • Laggy interface
  • FPS counter shows < 15

Solutions:

  1. Reduce resolution:

    camera:
  width: 320
  height: 240

  2. Increase frame skip:

    frame_skip_base: 5

  3. Disable face mesh:

    visual_feedback:
  show_face_mesh: false
  show_skeleton: false

  4. Use CPU-only mode:

    gpu_acceleration:
  enabled: false

Problem: Emotion Detection Not Working

Symptoms:

  • Emotion always "neutral"
  • Low confidence scores
  • Error logs from DeepFace

Solutions:

  1. Check DeepFace installation:

    python -c "from deepface import DeepFace; print('OK')"

  2. Verify TensorFlow:

    python -c "import tensorflow as tf; print(tf.__version__)"
# Expected: 2.15.0

  3. Check lighting:

    • Pastikan wajah terilluminasi dengan baik
    • Hindar backlighting
    • Gunakan lighting tambahan jika perlu

  4. Adjust confidence threshold:

    emotion:
  min_confidence: 0.2  # Lower untuk lebih sensitive

Problem: GPU Not Utilized

Symptoms:

  • High CPU usage, low GPU usage
  • nvidia-smi shows 0% GPU utilization
  • TensorFlow/PyTorch using CPU

Solutions:

  1. Verify CUDA installation:

    nvidia-smi
# Check CUDA Version column

  2. Check PyTorch CUDA:

    python -c "import torch; print('CUDA:', torch.cuda.is_available())"
# Expected: CUDA: True

  3. Reinstall PyTorch with CUDA:

    pip uninstall torch torchvision
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Problem: Application Won't Start

Symptoms:

  • "Address already in use"
  • "Port 8080 already occupied"
  • Connection refused

Solutions:

  1. Kill existing process:

    # Windows
taskkill /f /im python.exe

# Linux/macOS
pkill -f "python app.py"

  2. Change port:

    # Edit app.py line ~215
socketio.run(app, host='0.0.0.0', port=8081)  # Use 8081

  3. Check firewall:

    • Windows: Allow Python through Windows Firewall
    • Linux: sudo ufw allow 8080

❓ FAQ

Q: Apakah EAGLEARN menyimpan video saya?

A: TIDAK. EAGLEARN melakukan local processing only. Tidak ada video atau gambar yang di-upload ke cloud. Semua processing terjadi di komputer Anda.

Q: Berapa latensi aplikasi?

A:

  • Video feed: ~50-100ms (camera ke browser)
  • Emotion detection: 100-200ms (DeepFace inference)
  • Total latency: ~150-300ms
  • Dengan temporal smoothing, ada tambahan 150-300ms untuk stabilisasi

Q: Bisakah digunakan tanpa internet?

A: YA. EAGLEARN bekerja 100% offline. Koneksi internet hanya diperlukan saat pertama kali untuk mendownload model AI (DeepFace weights).

Q: Berapa akurasi emotion detection?

A:

  • DeepFace (SSD backend): ~85-90% accuracy
  • DeepFace (RetinaFace): ~95% accuracy (dengan GPU)
  • F1-score: 0.87-0.93 tergantung dataset

Q: Bisakah mendeteksi multiple people?

A: Saat ini EAGLEARN dirancang untuk single user (one face di frame). Untuk multiple users, perlu modifikasi kode.

Q: Berapa resource usage?

A:

  • CPU: 30-60% (Intel i5/Ryzen 5)
  • RAM: 1-2 GB (Python + models)
  • GPU: 2-3 GB VRAM (jika ada GPU)
  • Storage: 500 MB untuk models

Q: Bagaimana cara improve akurasi?

A:

  1. Lakukan calibration sebelum session
  2. Pencahayaan baik - wajah terlihat jelas
  3. Posisi proper - wajah langsung menghadap kamera
  4. Gunakan GPU untuk RetinaFace backend
  5. Adjust thresholds di config.yaml

Q: Apakah data privacy terjamin?

A: YA. Semua data diproses secara lokal. Tidak ada data yang dikirim ke server eksternal. Session data disimpan di lokal komputer Anda.

📚 TIPS & TRICKS

Best Practices:

  1. Lighting - Gunakan pencahayaan diffuse, hindar shadows di wajah
  2. Position - Dudur pada jarak 50-80 cm dari kamera
  3. Background - Gunakan background solid/bersih
  4. Calibration - Lakukan kalibrasi sebelum sesi penting
  5. Breaks - Ambil break setiap 45-60 menit untuk hasil terbaik

Performance Tips:

  1. Close unused apps - Free up resources
  2. Use wired connection - Untuk network session (jika applicable)
  3. Disable visual feedback - Untuk low-end devices
  4. Adjust FPS target - Set ke 20-25 untuk battery saving

Accuracy Tips:

  1. Stable position - Hindar excessive movement
  2. Direct gaze - Pandang lurus ke kamera/screen
  3. Neutral expression - Untuk baseline calibration
  4. Multiple sessions - Rata-ratakan dari beberapa session

🔗 LINKS & RESOURCES

Documentation:

External Resources:

Support:

📝 CHANGELOG

Version 1.0 (8 Januari 2026)

  • ✅ Initial release
  • ✅ Focus monitoring with 85-95% accuracy
  • ✅ Real-time emotion detection (7 emotions)
  • ✅ Head pose, eye tracking, posture analysis
  • ✅ Temporal smoothing untuk stabilisasi
  • ✅ Calibration system
  • ✅ Privacy-first design (local processing only)
  • ✅ GPU acceleration support (PyTorch CUDA)

📄 LICENSE

This project is licensed under the MIT License - see the LICENSE file for details.

End of User Guide

Untuk pertanyaan lebih lanjut, hubungi development team atau lihat dokumentasi teknis di repository ini.

Happy Learning with EAGLEARN! 🚀📚