Implementation of Arcface in Employee Attendance System Face Recognition Based
DOI:
https://doi.org/10.61536/ambidextrous.v5i02.646Keywords:
Attendance system, facial recognition, ArcFace, geofencing, InsightFaceAbstract
The employee attendance process is still largely done manually, potentially leading to recording errors, attendance misuse, and low efficiency in attendance data management. This study aims to implement a facial recognition-based employee attendance system using the ArcFace method through the InsightFace framework combined with location validation using geofencing. The system was developed web-based using FastAPI as the backend, Bootstrap 5 and JavaScript as the frontend, MySQL as the database, and SCRFD for face detection. System testing was conducted using the Black Box Testing method to test the system's main functions, while user evaluation was conducted using a questionnaire with a Likert scale. The test results showed that all system functions run according to the established functional requirements. In addition, the results of the user evaluation showed that all respondents gave a positive assessment of the system, indicating that the system is easy to use and well-received by users. Thus, the developed system is able to support the employee attendance process more effectively through the implementation of facial recognition, location validation, and web-based attendance data management.
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References
D. Shinta, "JITE (Journal of Informatics and Telecommunication Engineering)," vol. 7, no. January, pp. 424–434, 2024.
ID Gede, A. Pucanganom, G. Arna, J. Saskara, and KA Seputra, "GEOFENCING AND FACE RECOGNITION IN THE PRESENCE SYSTEM CASE STUDY: SD SARASWATI 4 DENPASAR," vol. 14, no. 2, 2026.
J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” IEEE Trans. Anal Patterns. Mach. Intel., vol. 44, no. 10, pp. 5962–5979, 2022, doi: 10.1109/TPAMI.2021.3087709.
J. Deng, “Masked Face Recognition Challenge: The InsightFace Track Report,” 2021.
GC Terampe, “FACIAL RECOGNITION PERFORMANCE EVALUATION WITH YOLOV8 , ARCFACE , AND SVM IN A CONTACTLESS EMPLOYEE,” vol. 8, no. 1, 2025.
BK Putra, “YOLOv8 and FaceNet Algorithm for Real-Time Face Recognition Attendance System,” 2025.
A. Fu, A. Prianggono, B. Juliartha, M. Putra, and B. Hikmawan, “Development of a Student Attendance Monitoring System Using Yolo Object Detection and Opencv Face Recognition,” vol. 18, no. 1, pp. 84–87, 2024.
BW Nurlita et al., “COMPARISON OF ARCFACE AND DLIB PERFORMANCE IN FACE RECOGNITION WITH DETECTION USING YOLOV8,” pp. 890–903, 2024.
D. Ayu, D. Syaiful, and A. Ramelan, “Design and Construction of a Student Attendance System Tool Using Face Recognition with the YOLO Method Based on Raspberry Pi,” vol. 6, no. 2, 2024.
ND Hasan, AM Abdulazeez, and K. Region, "Indonesian Journal of Computer Science," vol. 13, no. 1, pp. 3779–3797, 2024.
N. Surantha and B. Sugijakko, "Internet of Things Lightweight face recognition-based portable attendance system with liveness detection," Internet of Things, vol. 25, no. January, p. 101089, 2024, doi: 10.1016/j.iot.2024.101089.
DA Using, F. Satria, D. Hamdhana, and L. Rosnita, “Student Attendance System Based on Real-Time Facial Recognition with,” vol. 13, no. 1, pp. 441–450, 2026, doi: 10.30865/jurikom.v13i1.9502.
J. Chen, “A Review of Face Recognition Technology : From Algorithms to Real-Time Systems with InsightFace,” pp. 1–8, 2025.
C. Juanda and Paska, "JU-KOMI DEVELOPMENT OF A FACE RECOGNITION AND GEOFENCING JU-KOMI," vol. 3, no. 01, pp. 66–81, 2025.
N. Apriliyani, E. Setiawan, and A. Muchayan, “Implementation of Agile Methods in Developing Web-Based Cultural Introduction Applications,” J. Comput. Sci. and Business, vol. 13, no. 1, pp. 8–21, 2022, doi: 10.47927/jikb.v13i1.261.
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Copyright (c) 2026 Rifky Andika Fajrul Falah, Ronny Makhfuddin Akbar, Ahmad Syaifuddin
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