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Lestari, Wirna
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Computer Science & IT

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Implementation of Deep Learning using the Convolutional Neural Network (CNN) Method to Improve Attedance List Lestari, Wirna; Khair, Rizaldy
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit58

Abstract

Efficient and accurate employee attendance recording is a vital aspect of human resource management, including within the Faculty of Computer Science and Information Technology, Universitas Muhammadiyah Sumatera Utara (FIKTI UMSU). This study focuses on enhancing the efficiency of the attendance system through the application of Deep Learning techniques, particularly the Convolutional Neural Network (CNN), which serves to automatically detect and recognise faces from visual data. The web-based application developed in this research employs programming languages such as Python, HTML, PHP, CSS, and JavaScript, with MySQL as the database system, and is designed to support two user roles: administrator and end-user. The findings indicate that the implementation of the CNN method enables real-time image processing, reduces the potential for fraud in manual attendance, and improves the accuracy and efficiency of attendance recording. Based on testing, the application functions effectively, provides a user-friendly interface, and is capable of delivering reliable automated attendance documentation.
Co-Authors Rizaldy Khair