Vol 2 No 1 (March 2026)
Articles

An Intelligent Student Attendance System Based on Facial Image Recognition Using YOLOv5: A Case Study at Politeknik Negeri Lhokseumawe

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  • Heri Maulana,
  • Azhar Azhar,
  • Rahmad Hidayat,
  • Eni Mawardhaningrum
Heri Maulana
Jurusan Teknologi Informasi dan Komputer, Politeknik Negeri lhokseumawe
Azhar Azhar
Jurusan Teknologi Informasi dan Komputer, Politeknik Negeri lhokseumawe
Rahmad Hidayat
Jurusan Teknologi Informasi dan Komputer, Politeknik Negeri lhokseumawe
Eni Mawardhaningrum
Sekolah Pascasarjana, Universitas Muhammadiyah Yogyakarta, Indonesia

Published 24-03-2026

Keywords

  • attendance,
  • student,
  • recognition,
  • face,
  • image,
  • YOLO5s
    • ...More
    Less

Copyright (c) 2026 Heri Maulana, Azhar Azhar, Rahmad Hidayat, Eni Mawardhaningrum

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

You Only Look Once (YOLO) is an effective deep learning method for real-time object detection. This method is applied to build an accurate, fast facial recognition model, with a case study on an attendance system based on facial images at the Lhokseumawe State Polytechnic. The main objective is to implement and evaluate the YOLOv5s model's performance on the attendance system using facial images with high accuracy. The research process includes collecting facial image datasets from 40 subjects with various viewing angles to train the YOLOv5s model. This model is specifically configured to detect one class of objects, namely faces, and then integrated into the system to function as the main face detector. Model performance is evaluated quantitatively using a confusion matrix to measure key metrics such as accuracy, precision, recall, and F1 score. The evaluation results show that the developed YOLOv5s model has excellent performance. This model achieved 90% accuracy, with a precision and recall of 92%. The balanced F1-score value (92%) proves that the YOLOv5s method has a high level of accuracy for detecting faces. The high-performance metrics confirm that this method is the right solution for building an attendance system based on accurate facial images.

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