Implementasi YOLOv8 dan Local Binary Pattern Histogram (LBPH) untuk Simulasi Presensi

Romadhon, Muhammad (2025) Implementasi YOLOv8 dan Local Binary Pattern Histogram (LBPH) untuk Simulasi Presensi. Router : Jurnal Teknik Informatika dan Terapan, 3 (3). pp. 1-13. ISSN 3032-3312

	Text Lembar persetujuan publikasi artikel.pdf Download (798kB)
	Text (Artikel Publikasi) ROUTER+VOL+3+NO+3+SEPTEMBER+2025+HAL+01-13.pdf Download (1MB)

Abstract

Attendance is an essential activity in both educational institutions and companies, serving as an indicator of discipline, presence, and individual responsibility. Conventional attendance systems that still rely on manual journals often face several problems, such as vulnerability to manipulation, data loss, and physical damage. Meanwhile, modern methods such as fingerprint, QR code, RFID, and GPS are not entirely ideal since each has its own limitations in terms of cost, accuracy, user convenience, and potential misuse. For instance, fingerprint systems raise hygiene concerns due to shared use, while QR code and GPS methods are prone to fraud and location spoofing. To address these challenges, this study proposes a face-based attendance simulation system by integrating the YOLOv8 algorithm for face detection and Local Binary Pattern Histogram (LBPH) for face recognition. YOLOv8 was chosen for its ability to detect faces in real time with high speed and accuracy, while LBPH is employed for face recognition due to its robustness in handling variations in facial features and its relatively low computational requirements. This makes the system efficient even when implemented on medium-specification devices. The system was tested on 25 participants with a total of 250 attendance attempts. Based on the confusion matrix analysis, the system achieved outstanding performance with 98.4% accuracy, 98.4% precision, 100% recall, and a 99.2% F1-score. Furthermore, the system automatically recorded attendance dates and times with an average latency of 69.185 ms, proving its capability to operate quickly and reliably in real-world scenarios. Nevertheless, several limitations were observed, such as decreased accuracy when the face moved too quickly during image capture, as well as potential performance degradation under extreme lighting conditions. Despite these challenges, the proposed system demonstrates excellent performance and offers a promising solution for efficient, hygienic, and fraud-resistant attendance management applicable to both educational and professional environments.

Item Type:	Article
Uncontrolled Keywords:	Attendance Recording; Face Detection; Face Recognition; LBPH; YOLOv8.
Subjects:	Engineering > Informatics Engineering Engineering
Divisions:	Faculty of Engineering > Informatics Engineering Study Program
Depositing User:	Muhammad Romadhon
Date Deposited:	08 Oct 2025 06:38
Last Modified:	08 Oct 2025 06:38
URI:	http://eprints.umg.ac.id/id/eprint/14869

Actions (login required)

View Item