RANCANG BANGUN SISTEM PRESENSI SISWA PADA MAS DARUL AMAN ACEH BESAR BERBASIS YOLOv8
DOI:
https://doi.org/10.22373/jintech.v7i2.9981Keywords:
Absensi, YOLOv8, Pengenalan Wajah, Visi Komputer, Sistem Berbasis WebAbstract
Abstract: The advancement of computer vision technology has created new opportunities in developing face recognition-based attendance systems. This study aims to design and implement a student attendance system at MAS Darul Aman Aceh Besar using YOLOv8 for face detection and FaceInsight for face recognition. The system is developed as a web-based application using the Laravel framework, integrated with a Flask service to handle the face detection process. Student data and attendance records are stored in a MySQL database, while the user interface is built using Blade Template Engine and TailwindCSS. System evaluation is conducted through black-box testing and accuracy testing of face recognition. The results indicate that the system is capable of detecting and recognizing faces automatically with an accuracy rate of up to 96%, while also recording attendance in real time. This implementation is expected to improve efficiency, enhance data accuracy, and reduce the possibility of attendance fraud in the school environment.
Keywords: Attendance, YOLOv8, Laravel, FaceInsight, Face Recognition
Abstrak: Perkembangan teknologi computer vision telah mendorong inovasi dalam sistem presensi berbasis pengenalan wajah. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem presensi siswa pada MAS Darul Aman Aceh Besar dengan memanfaatkan algoritma YOLOv8 untuk deteksi wajah dan FaceInsight untuk proses pengenalan wajah. Sistem dikembangkan berbasis web menggunakan framework Laravel yang terintegrasi dengan Flask sebagai layanan pemrosesan deteksi wajah. Data siswa dan presensi disimpan dalam basis data MySQL, sedangkan antarmuka dibangun menggunakan Blade Template Engine dan TailwindCSS. Metode pengujian yang digunakan adalah black-box testing serta pengujian akurasi pengenalan wajah. Hasil penelitian menunjukkan bahwa sistem mampu melakukan deteksi dan identifikasi wajah secara otomatis dengan tingkat akurasi mencapai 96% serta mampu mencatat kehadiran secara real-time. Implementasi sistem ini diharapkan dapat meningkatkan efisiensi dan mengurangi potensi kecurangan dalam proses presensi siswa.
Kata kunci: Presensi, YOLOv8, Laravel, FaceInsight, Pengenalan Wajah.
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