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Journal : joincs journal of informatics network and computer science

Development of an Automated Attendance System Based on Facial Recognition Using Convolutional Neural Networks (CNN) for Kaca Super Jaya MSME: Pengembangan Sistem Kehadiran Otomatis Menggunakan Pengenalan Wajah Menggunakan Convolutional Neural Network (CNN) terhadap UMKM Kaca Super Jaya Syaeful Anas Aklani; Jetset; Suwarno Suwarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1692

Abstract

Attendance management is a critical component of human resource administration, yet conventional methods such as manual sign-in sheets and card-based systems are often inefficient, error-prone, and vulnerable to manipulation. This study aims to design and implement an automatic attendance system based on face recognition using Convolutional Neural Networks (CNN) for UMKM Kaca Super Jaya. The proposed system replaces manual attendance by enabling real-time, contactless, and automated attendance recording through facial identification. An applied research approach with qualitative methods was employed, involving system development, direct observation, and structured interviews with users. The CNN model was trained using facial image datasets under various conditions, including different lighting levels, facial expressions, and viewing angles, to improve robustness and accuracy. The system architecture integrates a camera as input, a CNN-based face recognition model, a backend server, and a web-based dashboard for attendance monitoring and reporting. Experimental results show that the system achieved an average face recognition accuracy of 96%, demonstrating reliable performance even under suboptimal lighting and non-frontal face angles. The implementation significantly reduced attendance processing time, minimized human error, and lowered the potential for fraudulent practices such as proxy attendance. These findings indicate that CNN-based face recognition is an effective and practical solution for enhancing attendance management efficiency and accuracy in small and medium enterprises.
Co-Authors Afandi Afandi Afandi Alviana Alviana Amalia Putri Yulandi Anderson Arvando Andry Andry Annisya Putri Nadhia Annisya Putri Nadhia Ari Firmansah Arief Fernando Brain Gantoro Caca Natasya Calvin Chin Chris Tan Christian, Yefta Daniel Adventus Davina Davina Deli Deli Derrick Derrick Dessy Amelia Dimas Firmansyah Nasution Dirson Wiratama Edi Santoso Erwin Evi Yanti Felix King Lie Fenky Fenky Gracea Venice Hendi Hendi Herman Jeffrey Rustandi Jervis William Jesen Jeverlino Jessica Christina Jessica Novia Jetset Jocelyn Jocelyn Jocelyn Jocelyn Joen Lie Jon Susanto Jonathan Jonathan Jonathan Jonathan Joyslin Joyslin Julianto Julianto Juven Gautama Juven Gautama Kevin Gautama Kevin Indra Bhaskara Kevin kevin Kristianti Kristianti Kristianti Kristianti Malvin Huang Marvin Christian Marvin Christian Melna Caintan Melvan Melvan Melvin Melvin Melvy Devalia Mike Sonobe Pangihutannasa Moch Ihda Farhan Effendi Muhammad Faiz Mungkap Mangapul Siahaan Muthia Andini Philander Alvando Davian Ratu Olivia Ricky Fernando Rio Fernando Rio Riferro Lim Roma Sabet Manurung Roma Sebet Manurung Ryo Kusnadi Sama, Hendi Stephanie Stephanie Syaeful Anas Aklani, Syaeful Teddy Sanjaya Tedy Fernando Valene Fortuna Lim Vanessa Riarta Atmaja Vendryan Vendryan Veni Sisca Verren Calystania Vicco Leonardo Vicky Tantri Vincent Eng Vincent Vincent Vinson Vinson Violen Anjeli Anggraini Violin Anjeli Anggraini Vionna Vionna Vira Vira Wendy Wendy William Surya Jaya William Surya Jaya Yudi Hartanto Yudi Hartanto