Sistem Monitoring Kehadiran Perkuliahan Menggunakan Face Detection Dengan Algoritma Viola Jones

  • Zul Fachmi STMIK Bandung Bali
  • Made Sudarma
  • Lie Jasa


Presence of lectures is an important factors to take the final exam. It is necessary to have a presence system with computer vision technology that is capable to handling problems manually. Computer vision technology used is face detection and recognition in order to monitor attendance data system. The face detection process in this study uses the Viola-Jones algorithm, and this algorithm has four stages, namely Haar Like Feature, Integral Image, Adaboost learning and Cascade classifier. The results of this study Viola-Jones algorithm successfully applied to the face detection process and in the face recognition process using the KNN (K-Nearest Neighbor) method with an accuracy rate of 94.79%.


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[1] N. Dhanalakshmi, S. G. Kumar, and Y. P. Sai, “Aadhaar based biometric attendance system using wireless fingerprint terminals,” Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, pp. 651–655, 2017.
[2] P. S. S. Srivignessh and M. Bhaskar, “RFID and pose invariant face verification based automated classroom attendance system,” Int. Conf. Microelectron. Comput. Commun. MicroCom 2016, 2016.
[3] M. Sajid et al., “Automated Attendance System using Machine Learning Approach,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 3, no. 1, pp. 501–505, 2017.
[4] N. K. Jayant and S. Borra, “Attendance Management System Using Hybrid Face Recognition Techniques,” 2016 Conf. Adv. Signal Process., pp. 412–417, 2016.
[5] D. P. S. Mangayarkarasi Nehru, “Illumination invariant face detection,” Int. Conf. Adv. Comput. Commun. Syst. (ICACCS -2017), vol. Jan. 06-07, pp. 4–7, 2017.
[6] P. B. S. Dodit Suprianto, Rini Nur Hasanah, “Sistem Pengenalan Wajah Secara Real-Time dengan Adaboost, Eigenface PCA & MySQL,” J. EECCIS (Electrics, Electron. Commun. Control. Informatics, Syst., vol. 7, no. 2, pp. 179–184, 2013.
[7] C. Suhery and I. Ruslianto, “Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis ( PCA ),” J. Edukasi dan Penelit. Inform., vol. 3, no. 1, pp. 9–15, 2017.
[8] B. S. M. Zufar, “Convolutional Neural Networks untuk Pengenalan Wajah Secara Real - Time,” J. SAINS dan SENI ITS, vol. 5, no. 2, pp. 72–77, 2016.
[9] P. S. Chandak, M. Patange, H. Deshpande, S. Maredia, and P. Bagwe, “A Prototype of Automated Attendance System Using Image Processing,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 5, no. 4, pp. 501–505, 2016.
[10] E. Rekha and P. Ramaprasad, “An efficient automated attendance management system based on Eigen Face recognition,” Proc. 7th Int. Conf. Conflu. 2017 Cloud Comput. Data Sci. Eng., vol. 5, pp. 605–608, 2017.
[11] A. Hendrotriatmoko, S. Hadi, and H. S. Dachlan, “Penggunaan Metode Viola-Jones dan Algoritma Eigen Eyes dalam Sistem Kehadiran Pegawai,” J. EECCIS, vol. 8, no. 1, pp. 41–46, 2014.
[12] J. Efendi, M. I. Zul, and W. Yunanto, “Real Time Face Recognition using Eigenface and Viola-Jones Face Detector,” Int. J. Informatics Vis., vol. 1, no. 1, pp. 16–22, 2017.
[13] D. Indra, “Pendeteksian tepi objek menggunakan metode gradien,” J. Ilm. Ilk., vol. 8, no. Agustus, pp. 69–75, 2016.
[14] T. Y. Prahudaya and A. Harjoko, “Metode Klasifikasi Mutu Jambu Biji Menggunakan Knn Berdasarkan Fitur Warna Dan Tekstur,” J. Teknosains, vol. 6, no. 2, p. 113, 2017.
How to Cite
FACHMI, Zul; SUDARMA, Made; JASA, Lie. Sistem Monitoring Kehadiran Perkuliahan Menggunakan Face Detection Dengan Algoritma Viola Jones. Majalah Ilmiah Teknologi Elektro, [S.l.], v. 18, n. 1, p. 119-126, may 2019. ISSN 2503-2372. Available at: <>. Date accessed: 03 june 2020. doi: