Employee Attendance System with Face Recognition

Nourman Satya Irjanto, Rully Oktavia Hermawan


The attendance recording method that uses a fingerprint recognition system is felt to be a means of spreading the covid 19 virus and it is necessary to create a biometric attendance recording system using facial recognition. This study aims to build an employee attendance system that uses the concept of facial recognition because the existing manual attendance system is time consuming and impractical to maintain. This system consists of four phases - database creation, face detection, face recognition, attendance updates. The database is created using employee photos. Face detection and recognition is carried out using the HOG algorithm for face classification and SVM classification for face recognition. Faces are detected and recognized from the live webcam. Attendance will be recorded automatically into the document. The system after being tested has good accuracy, reaching 86.4% in the morning, and 88.8% in the afternoon, which is quite high accuracy and in future research it is hoped that the system can be built online so that employees can access the attendance system anywhere. and reports can be accessed anywhere online.


Employee attendance; facial recognition; HOG; SVM Clasification; Python;

Full Text:

PDF (Indonesian)


A. Muntholib and S. Erlinda, “Prototipe Absensi STMIK Amik Riau Berbasis Face Recognition Menggunakan Metode Eigenface,” SATIN - Sains dan Teknol. Inf., vol. 4, no. 2, p. 76, 2019.

R. Samet and M. Tanriverdi, “Face recognition-based mobile automatic classroom attendance management system,” Proc. - 2017 Int. Conf. Cyberworlds, CW 2017 - Coop. with Eurographics Assoc. Int. Fed. Inf. Process. ACM SIGGRAPH, vol. 2017-Janua, no. September 2017, pp. 253–256, 2017.

R. Panca Juniawan, Fransiskus; Yuny Sylfania, Dwi; Septia Adiputra, “Integrasi Fingerprint System Dengan Real Time Absensi Dosen Berbasis Web (Studi Kasus : STMIK Pontianak),” CogITo Smart J., vol. 2, no. 2, p. 135, 2018.

G. D. P. Maramis and P. T. D. Rompas, “Radio Frequency Identification (RFID) Based Employee Attendance Management System,” IOP Conf. Ser. Mater. Sci. Eng., vol. 306, no. 1, 2018.

B. Soewito, F. L. Gaol, E. Simanjuntak, and F. E. Gunawan, “Smart mobile attendance system using voice recognition and fingerprint on smartphone,” Proceeding - 2016 Int. Semin. Intell. Technol. Its Appl. ISITIA 2016 Recent Trends Intell. Comput. Technol. Sustain. Energy, no. July, pp. 175–180, 2017.

D. Prangchumpol, “Face Recognition for Attendance Management System Using Multiple Sensors,” J. Phys. Conf. Ser., vol. 1335, no. 1, 2019.

P. Pasumarti and P. P. Sekhar, “Classroom Attendance Using Face Detection and Raspberry-Pi,” Int. Res. J. Eng. Technol., vol. 5, no. 1, pp. 167–171, 2018.

H. Yang and X. Han, “Face recognition attendance system based on real-time video processing,” IEEE Access, vol. 8, pp. 159143–159150, 2020.

H. Rathod, Y. Ware, S. Sane, S. Raulo, V. Pakhare, and I. A. Rizvi, “Automated attendance system using machine learning approach,” 2017 Int. Conf. Nascent Technol. Eng. ICNTE 2017 - Proc., vol. 5, no. 09, pp. 24–26, 2017.

M. S. Akbar, P. Sarker, A. T. Mansoor, A. M. Al Ashray, and J. Uddin, “Face Recognition and RFID Verified Attendance System,” Proc. - 2018 Int. Conf. Comput. Electron. Commun. Eng. iCCECE 2018, pp. 168–172, 2019.

Y. W. M. Yusof et al., “Face Recognition and RFID Verified Attendance System,” SISY 2017 - IEEE 15th Int. Symp. Intell. Syst. Informatics, Proc., vol. 5, no. 3, pp. 174–178, 2017.

M. Arsenovic, S. Sladojevic, A. Anderla, and D. Stefanovic, “FaceTime - Deep learning based face recognition attendance system,” SISY 2017 - IEEE 15th Int. Symp. Intell. Syst. Informatics, Proc., pp. 53–57, 2017.

F. Hamami, I. A. Dahlan, S. W. Prakosa, and K. F. Somantri, “Implementation Face Recognition Attendance Monitoring System for Lab Surveillance with Hash Encryption,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020.

D. Sunaryono, J. Siswantoro, and R. Anggoro, “An android based course attendance system using face recognition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 3, pp. 304–312, 2021.

A. Najmurrokhman, K. Kusnandar, A. B. Krama, E. C. Djamal, and R. Rahim, “Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone,” MATEC Web Conf., vol. 197, pp. 1–6, 2018.

Adam geitgey, “Modern Face Recognition with Deep Learning,” medium.com. [Online]. Available: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78.

P. E. Rybski, D. Huber, D. D. Morris, and R. Hoffman, “Visual classification of coarse vehicle orientation using histogram of oriented gradients features,” IEEE Intell. Veh. Symp. Proc., pp. 921–928, 2010.

K. Okokpujie, E. Noma-Osaghae, S. John, K. A. Grace, and I. P. Okokpujie, “A face recognition attendance system with GSM notification,” 2017 IEEE 3rd Int. Conf. Electro-Technology Natl. Dev. NIGERCON 2017, vol. 2018-Janua, pp. 239–244, 2017.

DOI: http://dx.doi.org/10.30700/jst.v12i2.1148

Article Metrics

Abstract view : 350 times
PDF (Indonesian) - 232 times


  • There are currently no refbacks.

Copyright (c) 2022 SISFOTENIKA

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

Badan Pengelola Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (SISFOTENIKA) STMIK Pontianak.


Jurnal Ilmiah SISFOTENIKA terindex di :











ISSN Printed : 2087-7897

ISSN Online : 2460-5344


Jurnal Ilmiah SISFOTENIKA Terakreditasi Peringkat Empat


Partners & Co-Organizers:

Lisensi Creative Commons

Jurnal Ilmiah SISFOTENIKA: STMIK Pontianak Online Journal ISSN Printed (2087-7897) - ISSN Online (2460-5344) licensed under a Lisensi Creative Commons Atribusi 4.0 Internasional. Flag Counter

View My Stats>