Penerapan Face Recognition pada Aplikasi Akademik Online

Main Article Content

Budi Tri Utomo
Iskandar Fitri
Eri Mardiani

Abstract

In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.

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Article Details

How to Cite
Utomo, B. T., Fitri, I., & Mardiani, E. (2021). Penerapan Face Recognition pada Aplikasi Akademik Online. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 5(4), 420–424. https://doi.org/10.35870/jtik.v5i4.244
Section
Computer & Communication Science
Author Biography

Budi Tri Utomo, Universitas Nasional

Teknik Informatika, Fakultas Teknologi dan Informatika

References

Yusuf, M., 2016. Rancang Bangun Aplikasi Absensi Perkuliahan Mahasiswa dengan Pengenalan Wajah (Doctoral dissertation, Institut Teknologi Sepuluh Nopember Surabaya).

Saputra, W.M., Wibawa, H.A. and Bahtiar, N., 2014. Pengenalan Wajah Menggunakan Algoritma Eigenface dan Euclidean Distance. Journal of Informatics and Technology, 2(1), pp.113-127.

Lino, A.F., Silva, B.C., Rocha, D.P., Furriel, G.P. and Calixto, W.P., 2017, October. Performance of haar and LBP features in cascade classifiers to whiteflies detection and counting. In 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) (pp. 1-6). IEEE.

Suhery, C. and Ruslianto, I., 2017. Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA). Jurnal Edukasi dan Penelitian Informatika (JEPIN) Vol, 3(1).

Zhao, X. and Wei, C., 2017, August. A real-time face recognition system based on the improved LBPH algorithm. In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP) (pp. 72-76). IEEE.

Wiryadinata, R., Istiyah, U., Fahrizal, R., Priswanto, P. and Wardoyo, S., 2017. Sistem Presensi Menggunakan Algoritme Eigenface dengan Deteksi Aksesoris dan Ekspresi Wajah. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 6(2), pp.222-229.

Mardiani, E., Rahmansyah, N., Kurniawan, H. and Sensuse, D.I., 2016. Kumpulan Latihan SQL. Elex Media Komputindo.

Abhirawa, H., Jondri, J. and Arifianto, A., 2017. Pengenalan Wajah Menggunakan Convolutional Neural Network. eProceedings of Engineering, 4(3).

Wahyudi, E., Kusuma, H. and Wirawan, W., 2011. Perbandingan unjuk kerja pengenalan wajah berbasis fitur local binary pattern dengan algoritma pca dan chi square. In Seminar Nasional Technology and ITS Aplications. Surabaya: Institut Teknologi Sepuluh Nopember Surabaya.

Efendi, J., Zul, M.I. and Yunanto, W., 2017. Real time face recognition using eigenface and Viola-Jones face detector. JOIV: International Journal on Informatics Visualization, 1(1), pp.16-22.

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