Rancang Bangun Sistem Presensi Pegawai Berbasis Face Recognition Menggunakan Algoritma Deep Learning (Studi Kasus: Kantor Desa Lingkok Berenge)
DOI:
https://doi.org/10.35746/jtim.v8i3.1073Keywords:
attendance system, face recognition, face descriptor, face-api.js, SIMADES-FACEAbstract
Employee attendance is an important aspect in village office personnel administration because it is related to discipline, attendance data management, and the preparation of administrative reports. However, the conventional attendance system at the Lingkok Berenge Village Office causes the re-capitulation process to be slow, prone to recording errors, and still allows for the practice of leaving attendance. This study aims to design, implement, and evaluate a face recognition-based employee attendance system to improve identification accuracy and support the digitalization of attendance administration. The developed system, SIMADES-FACE, was built using CodeIgniter 3, PHP, and MySQL and applies a face descriptor-based face recognition method using face-api.js run-ning on TensorFlow.js, with the process of face detection, feature extraction, and matching de-scriptors to biometric data stored in the database. The study used a Research and Development (R&D) method with a quantitative descriptive approach. System evaluation was carried out through black-box testing, measuring facial recognition accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and detection time. The test results showed an accuracy rate of 93.33% from 180 trials, a FAR value of 0%, an FRR of 6.67%, and an average detection time of 2.26 seconds. These results indicate that SIMADES-FACE is able to support the employee attendance process more accurately, efficiently, and digitally documented.
Downloads
References
Y. Malabi, M. Hani’ah, Noprianto, V. N. Wijayaningrum, V. Al Hadid Firdaus, and A. Himawan, “Efficient Employee At-tendance System Integrating RFID and Android-Based Face Recognition with Liveness Detection,” in 2024 International Conference on Electrical and Information Technology (IEIT), IEEE, Sep. 2024, pp. 163–168. https://doi.org/10.1109/IEIT64341.2024.10763296.
C. Mittal, S. Gupta, V. Tayal, and P. Kumar, “Automated attendance system using real-time face recognition,” Int. J. Res. Eng. Innov., vol. 09, no. 03, pp. 116–122, 2025, https://doi.org/10.36037/IJREI.2025.9305.
A. Autade et al., “Automated Multi Face Recognition and Identification using Facenet and VGG-16 on Real-World Dataset for Attendance Monitoring System,” 2023 7th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2023, 2023, https://doi.org/10.1109/ICCUBEA58933.2023.10392198.
[4] K. Sahu, “iAttend: A Real-Time Attendance Monitoring System using Face Recognition Technique,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 13, no. 4, pp. 6567–6575, 2025, https://doi.org/10.22214/ijraset.2025.69888.
M. H. M. Kamil, N. Zaini, L. Mazalan, and A. H. Ahamad, “Online attendance system based on facial recognition with face mask detection,” Multimed. Tools Appl., vol. 82, no. 22, pp. 34437–34457, 2023, https://doi.org/10.1007/s11042-023-14842-y.
A. Kumar, J. Sharma, and P. N. Renjith, “A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency,” 2025 IEEE Int. Students’ Conf. Electr. Electron. Comput. Sci. SCEECS 2025, 2025, https://doi.org/10.1109/SCEECS64059.2025.10940084.
R. Fernando and H. Athauda, “Image Processing based Real-Time Online Attendance Monitoring System using Facial Recognition,” 3rd Int. Conf. Image Process. Robot. ICIPRoB 2024 - Proc., 2024, https://doi.org/10.1109/ICIPRoB62548.2024.10543775.
M. Idris, R. Wijaya, T. Agung, and B. Wirayuda, “Employee Attendance System Based on Face Recognition and Liveness Detection Using MagFace,” Ind. Journal on Computing, vol. 10, no. February, pp. 32–43, 2026, https://doi.org/10.21108/indojc.v10i2.10294.
N. Surantha and B. Sugijakko, “Lightweight face recognition-based portable attendance system with liveness detection,” Internet of Things (Netherlands), vol. 25, 2024, https://doi.org/10.1016/j.iot.2024.101089.
A. Pawar, R. Hiwanj, P. Koparde, D. Chikmurge, and S. Barve, “Automated Employee Attendance Monitoring Using Liveness Face Recognition and Geofencing in Real Time,” Proc. IEEE 2023 5th Int. Conf. Adv. Electron. Comput. Commun. ICAECC 2023, 2023, https://doi.org/10.1109/ICAECC59324.2023.10560301.
K. Jha, A. Jain, and S. Srivastava, “Feature-level fusion of face and speech based multimodal biometric attendance system with liveness detection,” AIP Adv., vol. 14, no. 11, 2024, https://doi.org/10.1063/5.0234430.
S. Sawhney, K. Kacker, S. Jain, S. N. Singh, and R. Garg, “Real-time smart attendance system using face recognition tech-niques,” Proc. 9th Int. Conf. Cloud Comput. Data Sci. Eng. Conflu. 2019, pp. 522–525, 2019, https://doi.org/10.1109/CONFLUENCE.2019.8776934.
S. Huang and H. Luo, “Attendance System Based on Dynamic Face Recognition,” Proc. - 2020 Int. Conf. Commun. Inf. Syst. Comput. Eng. CISCE 2020, pp. 368–371, 2020, https://doi.org/10.1109/CISCE50729.2020.00081.
P. Chiranjeevi, L. Vivek, G. Varun Tej, B. Arjun Yadav, S. Likitha, “Real time face recognition system using deep learning techniques,” Int. Res. J. Mod. Eng. Technol. Sci., 2025, https://doi.org/10.56726/irjmets81141.
S. Almabdy and L. Elrefaei, “Deep convolutional neural network-based approaches for face recognition,” Appl. Sci., vol. 9, no. 20, 2019, https://doi.org/10.3390/app9204397.
Z. Chen, J. Chen, G. Ding, and H. Huang, “A lightweight CNN-based algorithm and implementation on embedded system for real-time face recognition,” Multimed. Syst., vol. 29, no. 1, pp. 129–138, 2023, https://doi.org/10.1007/s00530-022-00973-z.
Ms. Neelam, N. Sowjanya, K. Sharvani, V. Varshini “Facial Recognition Attendance System with Integrated Liveness Detection and Real-Time,” IJRASET, Volume 14 Issue III Mar 2026. https://doi.org/10.22214/IJRASET.2026.78994.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Riska Niswati Hasanah, Akbar Juliansyah, Jarir Jarir, Indriaturrahmi Indriaturrahmi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
