Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah Real-Time dengan Deteksi Anti-Spoofing Menggunakan YOLOv8 dan ArcFace
DOI:
https://doi.org/10.30865/jurikom.v13i1.9502Keywords:
Anti-Spoofing, ArcFace, Face Recognition, Student Attendance, YOLOv8Abstract
Student attendance recording is an important aspect in supporting discipline and administrative order in academic environments. Manual attendance methods still have several limitations, such as potential fraud and inefficient recapitulation processes. This study aims to develop a real-time face recognition-based student attendance system by implementing the YOLOv8 algorithm for face detection and ArcFace for identity recognition, complemented with an anti-spoofing feature to prevent fraudulent attempts. The system is designed to detect faces directly, recognize registered student identities, and record attendance automatically. The main contribution of this study lies in the integration of YOLOv8-based face detection, ArcFace-based face recognition, and an anti-spoofing mechanism into a single unified real-time attendance system. Experimental results show that the system successfully recognizes all registered students with a 100% success rate. The YOLOv8 anti-spoofing model demonstrates excellent performance in distinguishing real and fake faces, achieving an mAP@0.5 value of 0.995 and an F1-score close to 1. The system is also able to record attendance time in real time according to the actual time and present attendance data systematically. Based on these results, the developed real-time face recognition attendance system is accurate, secure, and feasible to be implemented as an attendance solution in academic environments
References
[1] M. A. Hosen, S. H. Moz, M. M. H. Khalid, S. S. Kabir, and S. M. Galib, “Face Recognition-Based Attendance System With Anti-Spoofing, System Alert, and Email Automation,” Radioelectron. Comput. Syst., vol. 2023, no. 2(106), pp. 119–128, 2023, doi: 10.32620/REKS.2023.2.10.
[2] K. H. Teoh, R. C. Ismail, S. Z. M. Naziri, R. Hussin, M. N. M. Isa, and M. S. S. M. Basir, “Face Recognition and Identification using Deep Learning Approach,” J. Phys. Conf. Ser., vol. 1755, no. 1, 2021, doi: 10.1088/1742-6596/1755/1/012006.
[3] N. Nurdin, D. Hamdhana, and M. J. Setiawan, “Sistem Pendeteksi Pola Lafadz Allah Dan Muhammad Pada Citra Al-Qur’an Menggunakan Metode Peirce” TECHSI., vol. 9, no. 2, 2017.
[4] H. Febrydias, and M. Subali, “Aplikasi Presensi Berbasis Face Recognition Dengan Metode Local Binary Patterns Histograms,” Seminar Nasional Teknologi Informasi dan Komunikasi., vol. 8, pp. 1–13, 2024.
[5] S. G. Ramdhani and E. I. Sela, “Implementasi Face Recognition Untuk Sistem Presensi Universitas Menggunakan Convolutional Neural Network,” Indones. J. Comput. Sci., vol. 12, no. 6, pp. 4098–4108, 2023, doi: 10.33022/ijcs.v12i6.3498.
[6] F. Yusuf, I. Lesmana, H. P. Bagja, “Implementasi Convolutional Neural Network (CNN) untuk Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah,” Jurnal Buffer Informatika., vol. 11, no. April, pp. 47–56, 2025. https://journal.fkom.uniku.ac.id/index.php/buffer
[7] C. Widjaya and A. Wicaksana, “Liveness Detection With Randomized Challenge-Response for Face Recognition Anti-Spoofing,” Int. J. Innov. Comput. Inf. Control, vol. 19, no. 2, pp. 419–430, 2023, doi: 10.24507/ijicic.19.02.419.
[8] S. Rao, Y. Huang, K. Cui, and Y. Li, “Anti-spoofing face recognition using a metasurface-based snapshot hyperspectral image sensor,” Optica, vol. 9, no. 11, p. 1253, 2022, doi: 10.1364/optica.469653.
[9] A. S. Savanth, K. G. R. Manish, P. Narayan, M. L. Nikhil, and V. G. Gokul, “Face Recognition System with 2D Anti-Spoofing,” in 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), 2022, pp. 226–230. doi: 10.1109/AIC55036.2022.9848909.
[10] R. V. S. Vardhan, S. Varun, and N. R. Krishnamoorthy, “Detection of Anti-Spoofing Face Using Yolo,” vol. 7, no. 2, pp. 1–7, 2025.
[11] L. Susanti, N. K. Daulay, and B. Intan, “Sistem Absensi Mahasiswa Berbasis Pengenalan Wajah Menggunakan Algoritma YOLOv5,” JURIKOM (Jurnal Riset Komputer)., 2023,. vol. 10, no. 2, 640-647, http://www.stmik-budidarma.ac.id/ejurnal/index.php/jurikom/article/view/6032
[12] S. Sayed, S. Fatima, and Q. Abdul, “Face Recognition Attendance System with Anti- Spoofing Detection,” international Journal of Advanced Research in Science, Communication and Technology, pp. 230–238, 2025, doi: 10.48175/IJARSCT-25839.
[13] D. Ayu, D. Syaiful, and A. Ramelan, “Rancang Bangun Alat Sistem Absensi Mahasiswa menggunakan Face Recognition dengan Metode YOLO berbasis Raspberry Pi,” Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer
ISSN- vol. 6, no. 2, 2024.
[14] R. Khana, A. E. Saputra, and M. Sobirin, “Implementasi Sistem Presensi Deteksi Wajah Menggunakan YOLOv5,” vol. 1, no. Maret, pp. 54–66, 2024.
[15] Y. Yanto, F. Aziz, and I. Irmawati, “Yolo-V8 Peningkatan Algoritma Untuk Deteksi Pemakaian Masker Wajah,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 3, pp. 1437–1444, 2023, doi: 10.36040/jati.v7i3.7047.
[16] H. Qin, M. Li, and J. Jin, “A Study on Forged Face Recognition based on Improved YOLOV8,” in 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE), 2024, pp. 494–498. doi: 10.1109/CBASE64041.2024.10824450.
[17] T. Ardiansyah, S. P. Balqis, and J. Haqqoni, “Deteksi Wajah dalam Foto Menggunakan Teknologi Visi Komputer,” no. 6, pp. 32–39, 2024.
[18] J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace : Additive Angular Margin Loss for Deep Face Recognition,” vol. 14, no. 8, pp. 1–17, 2015.
[19] T. V. Dang, “Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network,” J. Robot. Control, vol. 3, no. 6, pp. 754–761, 2022, doi: 10.18196/jrc.v3i6.15978.
[20] B. W. Nurlita et al., “Perbandingan Kinerja Arcface Dan Dlib Dalam Pengenalan Wajah Dengan Deteksi Menggunakan Yolov8,” pp. 890–903, 2024.
[21] I. Astuti, L. N. Ibrahim, W. W. Ariestya, and S. Ruhama, “Deep Learning for Horticulture : Convolutional Neural Network Driven Classification of Banana Types,” vol. x, no. 1, pp. 12–25, 2025.
[22] C. Juliandy and D. Darwin, “Pengenalan Computer Vision Berbasis Convolutional Neural Network melalui Dewatalks,” Sarwahita, vol. 21, no. 01, pp. 45–52, 2024, doi: 10.21009/sarwahita.211.4.
[23] J. Afrita, “Peran artificial intelligence dalam meningkatkan efisiensi dan efektifitas sistem pendidikan,” COMSERVA J. Penelit. Dan …, 2023, [Online]. Available: https://comserva.publikasiindonesia.id/index.php/comserva/article/view/731
[24] J. Jonathan and D. Hermanto, “Penentuan Epochs Hasil Model Terbaik : Studi Kasus Algoritma YOLOv8,” vol. 4, no. 2, pp. 792–798, 2024.
[25] B. Xu et al., “A cattle face identification approach based on RetinaFace and ArcFace loss., 2022, 193,” DOI https//doi. org/10.1016/j. compag, p. 106675, 2021.
[26] C. Lubis, “Deteksi YOLOv8 Dan Pengenalan Wajah Menggunakan RESNET50 Pada Gereja,” vol. 12, no. 1, pp. 130–140, 2025.
