HRIS Attendance System | GPS-Based Employee Tracking with Face Recognition

Modern HRIS Dashboard for attendance management with GPS geofencing, AI-powered face recognition, shift scheduling, and real-time monitoring

Keywords: HRIS, Attendance System, GPS Tracking, Face Recognition, AI, Employee Management, Next.js, PostgreSQL, FastAPI, InsightFace

HRIS Dashboard - Sistem Absensi GPS & Pengenalan Wajah

Aplikasi HRIS (Human Resource Information System) untuk manajemen absensi karyawan menggunakan GPS geolocation dan AI face recognition dengan validasi radius kantor dan verifikasi identitas berbasis wajah.

🚀 Fitur Utama

Untuk Admin:

✅ Dashboard monitoring real-time
👥 Manajemen karyawan (CRUD)
📸 Registrasi wajah karyawan (saat create/edit)
🕐 Manajemen shift (CRUD)
📅 Penjadwalan shift karyawan (per minggu)
📊 Data absensi lengkap dengan filter tanggal
📈 Statistik absensi (total, hadir, terlambat, alpha)
📥 Export laporan ke CSV dengan shift schedule yang benar
📍 Pengaturan multi-lokasi kantor
⚙️ Pengaturan sistem
🔒 Role-based access control

Untuk Karyawan:

✅ Check-in dengan validasi GPS + Face Recognition
✅ Check-out dengan validasi GPS + Face Recognition
🤖 Deteksi wajah otomatis dengan countdown
🎯 Verifikasi identitas real-time (similarity > 85%)
📍 Validasi radius multi-lokasi kantor (100m)
📊 Lihat status absensi hari ini
📅 Lihat jadwal shift bulanan
📈 Statistik absensi bulanan pribadi
⏰ Deteksi keterlambatan otomatis
🌍 Timezone WIB (Asia/Jakarta)

🛠️ Teknologi Stack

Frontend & Backend

Next.js 16 (App Router, JavaScript, Turbopack)
React 19 dengan Server Components
TailwindCSS 4 untuk styling modern

Database & ORM

PostgreSQL 16 (Dockerized)
Drizzle ORM dengan camelCase schema

AI & Face Recognition

FastAPI 0.115 (Python web framework)
InsightFace 0.7.3 (buffalo_l model, 512-dim embeddings)
ONNX Runtime 1.19 untuk inference
OpenCV 4.10 untuk image processing

Authentication & Security

JWT session-based cookies (HTTP-Only)
bcrypt untuk password hashing
Face verification dengan cosine similarity (threshold 0.5)

DevOps

Docker Compose multi-container setup
PostgreSQL container dengan persistent volume
Face Recognition API container terpisah

📋 Prerequisites

Node.js 18+ dan npm
Docker dan Docker Compose
Python 3.9+ (untuk face recognition service)
Browser dengan GPS dan Camera support
4GB RAM minimum (untuk ML model)

🔧 Instalasi & Setup

1. Clone Repository

git clone https://github.com/shadowanon46-maker/hris-attendance-system.git
cd hris-attendance-system

2. Install Dependencies

npm install

3. Setup Environment Variables

Buat file .env.local dengan konfigurasi berikut:

# PostgreSQL Configuration (for Docker Compose)
POSTGRES_USER=hrisadmin
POSTGRES_PASSWORD=hrispass123
POSTGRES_DB=hris_db
POSTGRES_PORT=5432

# Database Configuration
DATABASE_URL=postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@localhost:${POSTGRES_PORT}/${POSTGRES_DB}

# Session Secret (generate random string in production)
SESSION_SECRET=hris_secret_key_change_in_production_2024

# Face Recognition API URL
NEXT_PUBLIC_FACE_API_URL=http://localhost:8000

# Application Settings
NODE_ENV=development
NEXT_PUBLIC_APP_URL=http://localhost:3000

4. Start All Services dengan Docker Compose

docker-compose up -d

Ini akan menjalankan:

PostgreSQL di port 5432
Face Recognition API (FastAPI + InsightFace) di port 8000

Note: Pertama kali menjalankan, face recognition akan download model buffalo_l (~300MB). Tunggu hingga selesai (cek logs: docker logs -f hris_face_recognition)

5. Push Database Schema

npm run db:push

6. Seed Data Awal

npm run db:seed

7. Run Next.js Development Server

npm run dev

Aplikasi akan berjalan di: http://localhost:3000

🧪 Testing Face Recognition

Cek Face API Status

curl http://localhost:8000/health
# Response: {"status":"healthy","model":"buffalo_l"}

Test Face Detection

# Upload gambar untuk deteksi
curl -X POST "http://localhost:8000/detect" -F "file=@path/to/photo.jpg"

👤 Default Credentials

Admin

Email: admin@hris.com
Password: admin123

Employee Sample

Email: employee1@hris.com
Password: employee123

🗄️ Database Schema

Tabel: users

id, nip, email, password (hashed)
full_name, role (admin/employee)
shift_id, is_active
face_embedding (JSON, 512-dim vector)
face_registered_at (timestamp)
created_at, updated_at

Tabel: shift

id, name, start_time, end_time
tolerance_late, description
created_at, updated_at

Tabel: attendance

id, user_id, date
check_in_time, check_out_time
check_in_lat, check_in_lng
check_out_lat, check_out_lng
check_in_face_verified (boolean)
check_in_face_similarity (decimal)
check_out_face_verified (boolean)
check_out_face_similarity (decimal)
status, notes, created_at

Tabel: office_locations

id, name, latitude, longitude
radius, is_active
created_at, updated_at

Tabel: shift_schedule

id, user_id, shift_id
schedule_date, notes
created_by, created_at, updated_at

Tabel: activity_log

id, user_id, action
description, ip_address
user_agent, created_at

Tabel: company_settings

id, key, value
description, updated_at

🤖 Face Recognition System

Architecture

┌─────────────────┐      ┌──────────────────┐      ┌─────────────────┐
│   Next.js App   │─────▶│  FastAPI Server  │─────▶│  InsightFace    │
│  (Frontend/API) │◀─────│   (Port 8000)    │◀─────│   (buffalo_l)   │
└─────────────────┘      └──────────────────┘      └─────────────────┘
        │                        │
        │                        │
        ▼                        ▼
┌─────────────────┐      ┌──────────────────┐
│   PostgreSQL    │      │  Face Embeddings │
│   (Port 5432)   │      │   (512-d vector) │
└─────────────────┘      └──────────────────┘

Face Registration Flow

Admin membuka form create/edit karyawan
Klik "Daftar Wajah" untuk membuka camera
Sistem deteksi wajah secara real-time (brightness-based)
Countdown 3 detik setelah wajah terdeteksi
Auto-capture dan kirim ke FastAPI
InsightFace ekstrak embedding 512-dimensi
Simpan embedding ke database (JSON format)

Face Verification Flow (Clock In/Out)

Karyawan klik tombol Clock In/Out
Camera terbuka, sistem deteksi kehadiran wajah
Countdown 3 detik, auto-capture
Kirim foto + stored embedding ke FastAPI
FastAPI:
- Ekstrak embedding dari foto baru
- Hitung cosine similarity dengan embedding tersimpan
- Return verified=true jika similarity > 0.5 (50%)
Jika verified=true: Lanjut proses absensi
Jika verified=false: Tampilkan error 403 (Forbidden)

Model Details

Model: buffalo_l (InsightFace)
Embedding Size: 512 dimensions
Similarity Threshold: 0.5 (cosine similarity)
Typical Accuracy: 85-95% untuk wajah terdaftar
Detection Method: Brightness-based presence (60% center area)

🔐 Security Features

Password Hashing: bcrypt dengan salt rounds 10
Session Management: JWT-based secure cookies (HTTP-Only)
Role-based Access: Middleware untuk authorization
Face Recognition Security:
- Embedding disimpan sebagai JSON (tidak reversible ke gambar)
- Verifikasi mandatory untuk user dengan wajah terdaftar
- Threshold similarity 50% (adjustable)
- Reject attendance jika wajah tidak cocok (403 Forbidden)
GPS Geofencing: Multi-location support dengan radius validation
Activity Logging: Semua aksi penting dicatat
Timezone Consistency: Semua timestamp menggunakan WIB (UTC+7)

🌍 GPS Geofencing

Menggunakan Geolocation API browser
Validasi jarak menggunakan Haversine formula
Multi-location support: Bisa tambah lebih dari 1 lokasi kantor
Default radius: 100 meter dari koordinat (adjustable per lokasi)
Koordinat disimpan di office_locations table

⏰ Timezone Management

Semua operasi menggunakan WIB (Asia/Jakarta, UTC+7):

Clock In/Out timestamp
Query attendance by date
Export laporan
Dashboard statistics

Implementasi:

function getWIBDate() {
  const now = new Date();
  const wibOffset = 7 * 60; // WIB is UTC+7
  const utcTime = now.getTime() + (now.getTimezoneOffset() * 60000);
  return new Date(utcTime + (wibOffset * 60000));
}

📊 Fitur Laporan

Export CSV

Filter berdasarkan range tanggal
Format UTF-8 dengan BOM (Excel compatible)
Kolom: Tanggal, NIP, Nama, Shift (dari schedule), Check In, Check Out, Status
Perbaikan: Shift diambil dari shift_schedule, bukan user default shift
Auto-calculate status (present/late/absent)

🛠️ NPM Scripts

npm run dev          # Start Next.js development server
npm run build        # Build for production
npm run start        # Start production server
npm run db:push      # Push Drizzle schema to database
npm run db:seed      # Seed initial data (admin + employees)

🐳 Docker Commands

Start Services

docker-compose up -d              # Start semua services
docker-compose up -d postgres     # Start PostgreSQL saja
docker-compose up -d face-recognition  # Start Face API saja

Stop Services

docker-compose down               # Stop semua services
docker-compose stop postgres      # Stop PostgreSQL saja

View Logs

docker-compose logs -f            # All services
docker logs -f hris_postgres      # PostgreSQL logs
docker logs -f hris_face_recognition  # Face API logs

Rebuild Services

docker-compose build face-recognition  # Rebuild face API (jika ubah code)
docker-compose up -d face-recognition  # Restart dengan image baru

Database Management

# Masuk ke PostgreSQL console
docker exec -it hris_postgres psql -U hrisadmin -d hris_db

# Backup database
docker exec hris_postgres pg_dump -U hrisadmin hris_db > backup.sql

# Restore database
docker exec -i hris_postgres psql -U hrisadmin hris_db < backup.sql

📁 Struktur Project

project/hris/
├── src/
│   ├── app/
│   │   ├── api/                    # API Routes
│   │   │   ├── admin/              # Admin endpoints
│   │   │   │   ├── attendance/     # Attendance management
│   │   │   │   ├── dashboard/      # Dashboard stats
│   │   │   │   ├── employees/      # Employee CRUD
│   │   │   │   ├── export/         # CSV export
│   │   │   │   ├── settings/       # System settings
│   │   │   │   ├── shift-schedules/ # Schedule management
│   │   │   │   └── shifts/         # Shift CRUD
│   │   │   ├── auth/               # Authentication
│   │   │   │   ├── login/          # Login endpoint
│   │   │   │   ├── logout/         # Logout endpoint
│   │   │   │   ├── me/             # Get current user
│   │   │   │   └── register/       # Register endpoint
│   │   │   ├── attendance/         # Employee attendance
│   │   │   │   ├── clock-in/       # Clock in with GPS+Face
│   │   │   │   ├── clock-out/      # Clock out with GPS+Face
│   │   │   │   ├── status/         # Attendance status
│   │   │   │   └── today/          # Today attendance
│   │   │   └── employee/           # Employee endpoints
│   │   │       ├── monthly-stats/  # Monthly statistics
│   │   │       └── my-schedule/    # Employee schedule
│   │   ├── dashboard/              # Dashboard pages
│   │   │   ├── admin/              # Admin dashboard
│   │   │   │   ├── attendance/     # Attendance view
│   │   │   │   ├── employees/      # Employee management
│   │   │   │   ├── reports/        # Reports & export
│   │   │   │   ├── settings/       # Settings page
│   │   │   │   ├── shift-schedule/ # Schedule management
│   │   │   │   └── shifts/         # Shift management
│   │   │   └── page.js             # Employee dashboard
│   │   ├── login/                  # Login page
│   │   ├── register/               # Register page
│   │   ├── layout.js               # Root layout
│   │   └── globals.css             # Global styles
│   ├── components/                 # React components
│   │   ├── AdminNavbar.js          # Admin navigation
│   │   └── FaceCapture.js          # Face capture component
│   └── lib/                        # Libraries & utilities
│       ├── db.js                   # Drizzle database connection
│       ├── schema.js               # Drizzle schema (camelCase)
│       ├── session.js              # Session management
│       ├── geolocation.js          # GPS utilities (Haversine)
│       ├── seed.js                 # Database seeder
│       └── faceApi.js              # Face API utilities
├── face-recognition-service/       # FastAPI Face Recognition
│   ├── main.py                     # FastAPI application
│   ├── requirements.txt            # Python dependencies
│   └── Dockerfile                  # Docker build config
├── docker-compose.yml              # Multi-container setup
├── drizzle.config.js               # Drizzle ORM configuration
├── next.config.mjs                 # Next.js configuration
├── tailwind.config.js              # TailwindCSS configuration
├── package.json                    # Node dependencies
└── README.md                       # This file

🚨 Troubleshooting

Face Recognition Issues

Model tidak ter-download

# Cek logs face recognition container
docker logs -f hris_face_recognition

# Tunggu hingga muncul: "✅ Face Recognition models loaded successfully"
# Download model ~300MB, butuh waktu 1-5 menit tergantung koneksi

Face API tidak merespon

# Cek apakah container berjalan
docker ps | grep face

# Restart container
docker-compose restart face-recognition

# Rebuild jika ada perubahan code
docker-compose build face-recognition
docker-compose up -d face-recognition

Error "stored_embedding not received"

Pastikan FormData terkirim dengan benar
Cek parameter FastAPI menggunakan Form() bukan default parameter
Lihat debug logs: docker logs hris_face_recognition | grep "stored_embedding"

Similarity terlalu rendah (< 50%)

Pastikan pencahayaan cukup saat registrasi dan verifikasi
Hindari backlight atau cahaya terlalu gelap/terang
Minta user registrasi ulang wajah di kondisi pencahayaan normal

GPS Issues

GPS tidak berfungsi

Pastikan menggunakan HTTPS atau localhost
Izinkan akses lokasi di browser
Cek apakah device mendukung Geolocation API
Test di mobile untuk GPS lebih akurat

Selalu di luar radius padahal di lokasi

Cek koordinat office_locations table
GPS mobile bisa error ±20-50 meter
Pertimbangkan naikkan radius jadi 150-200 meter
Gunakan Google Maps untuk cek koordinat akurat

Database Issues

Connection error

# Cek container berjalan
docker ps

# Restart PostgreSQL
docker-compose restart postgres

# Cek DATABASE_URL di .env.local
echo $env:DATABASE_URL

Schema tidak sinkron

# Push schema ulang
npm run db:push

# Atau drop database dan recreate
docker-compose down -v
docker-compose up -d
npm run db:push
npm run db:seed

Session/Login Issues

Token expired atau invalid

Clear browser cookies
Cek SESSION_SECRET di .env.local
Logout dan login ulang

Timezone salah (tanggal tidak match)

Semua API sudah menggunakan WIB helper
Jika masih error, cek fungsi getWIBDate() di setiap API route
Refresh browser untuk fetch data dengan tanggal WIB

Performance Issues

Face API lambat

Model inference butuh ~500ms-1s per request
Pastikan container dapat alokasi memory cukup (min 2GB)
Jangan restart container saat model loading

Database query lambat

-- Tambah index untuk performa
CREATE INDEX idx_attendance_user_date ON attendance(user_id, date);
CREATE INDEX idx_shift_schedule_user_date ON shift_schedule(user_id, schedule_date);

📝 Changelog

Version 2.0 (Current)

✅ Face Recognition: AI-powered identity verification
✅ Multi-location GPS: Support multiple office locations
✅ Timezone WIB: Consistent timezone across all operations
✅ Shift Schedule Fix: Export CSV shows correct shift from schedule
✅ Auto-capture: Face detection dengan countdown otomatis
✅ Security: Mandatory face verification untuk user terdaftar

Version 1.0

✅ GPS-based attendance
✅ Shift management
✅ Employee CRUD
✅ Dashboard & reports

Built with ❤️ by Fitrah Andre

Tech Stack: Next.js 16 • PostgreSQL 16 • FastAPI • InsightFace • Docker

📄 License

MIT License - feel free to use for personal or commercial projects.

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

📧 Contact

GitHub: @shadowanon46-maker
Project: hris-attendance-system

⭐ Star this repo if you find it useful!

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
Docs		Docs
face-recognition-service		face-recognition-service
public		public
src		src
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml
drizzle.config.js		drizzle.config.js
eslint.config.mjs		eslint.config.mjs
init.sql		init.sql
jsconfig.json		jsconfig.json
migration_face_recognition.sql		migration_face_recognition.sql
next.config.mjs		next.config.mjs
package-lock.json		package-lock.json
package.json		package.json
postcss.config.mjs		postcss.config.mjs
seed_location.sql		seed_location.sql

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