Employee Face Recognition Using You Only Look Once version 5 (YOLOv5): A Case Study at a Transportation-Based University in Indonesia

DOI: https://doi.org/10.30996/jitcs.12278
Keywords: attendance system, face recognition, YOLOv5, You Only Look Once

Abstract

In 2020, Politeknik Perkerapian Indonesia Madiun (PPI Madiun) faced significant challenges due to the COVID-19 pandemic, which led to the implementation of both Work from Office (WFO) and Work From Home (WFH) policies. To support these policies, PPI Madiun utilized the Electronic Attendance and Remote Assignment Monitoring System (SKEMA RAJA) provided by the Indonesian Ministry of Transportation. However, this system lacked an integrated facial recognition feature to ensure the accuracy of attendance data. Facial recognition technology presents a viable solution to enhance the reliability and efficiency of this attendance system. One effective technology for face recognition is You Only Look Once version 5 (YOLOv5), which has been proven to detect objects with high speed and accuracy. This study aims to develop a face recognition system for PPI Madiun employees using YOLOv5. The results demonstrate that YOLOv5 can detect faces from multiple angles—including right, left, top, bottom, and front—with 100% accuracy under optimal conditions. Additionally, YOLOv5 successfully detects faces in real-time from varying distances (10 cm, 20 cm, and 30 cm), achieving 80% accuracy. The system's performance is influenced by factors such as the angle of capture, lighting conditions, and facial expressions.

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Author Biographies

Sunaryo Sunaryo, Politeknik Perkeretaapian Indonesia Madiun

Department of Railway Electrical Technology

Teguh Arifianto, Politeknik Perkeretaapian Indonesia Madiun

Department of Railway Electrical Technology

Muhammad Afif Amalul Arifidin, Politeknik Perkeretaapian Indonesia Madiun

Department of Railway Electrical Technology

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Published
2025-03-03
How to Cite
Sunaryo, S., Arifianto, T., & Arifidin, M. A. A. (2025). Employee Face Recognition Using You Only Look Once version 5 (YOLOv5): A Case Study at a Transportation-Based University in Indonesia. Journal of Information Technology and Cyber Security. https://doi.org/10.30996/jitcs.12278
Issue
2025
Section
Research Article

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