Lecturer Attendance System using Face Recognition Application an Android-Based

Feri Susanto; Fauziah Fauziah; Andrianingsih Andrianingsih

doi:10.47709/cnahpc.v3i2.981

Authors

Feri Susanto Universitas Nasional
Fauziah Fauziah Universitas Nasional
Andrianingsih Andrianingsih Universitas Nasional

DOI:

10.47709/cnahpc.v3i2.981

Keywords:

android, application system, attendance, face recognition, local binary pattern histogram

Dimension Badge Record

Abstract

In the field of industries, businesses, and offices the use of security systems and administrative management through data input using a face recognition system is being developed. Following the era of technological advances, communication and information systems are widely used in various administrative operational activities and company security systems because it is assessed by using a system that is based on facial recognition security levels and more secure data accuracy, the use of such systems is considered to have its characteristics so it is very difficult for other parties to be able to engineer and manipulate data produced as a tool to support the company's decision. Related to this, causing the author is to try to research the detection of facial recognition that is present in the application system through an Android device, then face recognition detection will be connected. and saved to the database that will be used as data about the presence of teaching lecturers. Using the local binary pattern histogram algorithm method to measure the face recognition system that can be applied as a technique in the attendance system of lecturers to be more effective and efficient. Based on testing by analyzing the false rate error rate and the false refusal rate can be seen that the average level of local binary pattern histogram accuracy reaches 95.71% better than through the Eigenface method which is equal to 76.28%.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 500 times

References

A. Fauzan, L. Vovamizanti, S.Si, and Y. N. F. (2018). Implementation Identification of Face Recognition Using LBPH (Local Binary Pattern Histogram) Method For Attendance Presence Based Android. EProceedings of Engineering, 5(3), 5403–5413.

Arya, S., Pratap, N., & Bhatia, K. (2015). Future of Face Recognition: A Review. Procedia Computer Science, 58, 578–585. https://doi.org/10.1016/j.procs.2015.08.076

D. Retnoningrum, A.W. Widodo, dan M. A. R. (2019). Ekstraksi Ciri Pada Telapak Tangan Dengan Metode Local Binary Pattern (LBP). 3(3), 2611–2618.

Efendi, J., Zul, M. I., & Yunanto, W. (2017). Real time face recognition using eigenface and viola-jones face detector. International Journal on Informatics Visualization, 1(1), 16–22. https://doi.org/10.30630/joiv.1.1.15

Fisika, B. (2012). Sistem Pengenalan Wajah Dengan Metode Eigenface Dan Jaringan Syaraf Tiruan (Jst). Berkala Fisika, 15(1), 15–20.

Gilang Aditya Rama, Fauziah, N. (2020). Perancangan Sistem Keamanan Brangkas Menggunakan Pengenalan Wajah Bderbasis Android. Media Informatika Budidarma, 4(3), 635–641.

Hermosilla, G., Rojas, M., Mendoza, J., Farías, G., Pizarro, F. T., Martín, C. S., & Vera, E. (2018). Particle Swarm Optimization for the Fusion of Thermal and Visible Descriptors in Face Recognition Systems. IEEE Access, 6, 42800–42811. https://doi.org/10.1109/ACCESS.2018.2850281

Marti, N. W., & Yota, K. A. E. (2016). Prototipe Sistem Absensi Berbasis Face Recognition Dengan Metode Eigenface. Seminar Nasional Vokasi Dan Teknologi, 451–456.

Mujib, K., Hidayatno, A., & Prakoso, T. (2018). Pengenalan Wajah Menggunakan Local Binary Pattern (Lbp) Dan Support Vector Machine (Svm). Transient, 7(1), 123. https://doi.org/10.14710/transient.7.1.123-130

Muliawan, M. R., Irawan, B., & Brianorman, Y. (2015). Implementasi Pengenalan Wajah Dengan Metode Eigenface Pada Sistem Absensi. Jurnal Coding, Sistem Komputer Untan, 03(1), 41–50. http://jurnal.untan.ac.id/index.php/jcskommipa/article/viewFile/9727/9500

Retno Choirunisa. (2021). Identifikasi Pola Wajah Menggunakan Metode Local Binary Pattern Histogram.

Rosita, E., & Wibowo, R. A. (2021). The Effect of the Interdependence of the Corporate Governance Mechanism on Firm Value in Manufacturing Companies Listed on the Indonesia Stock Exchange. Accounting and Finance …, Idx, 1–19. https://profesionalmudacendekia.com/index.php/afs/article/view/65

Santoso, K., & Kusuma, G. P. (2018). Face Recognition Using Modified OpenFace. Procedia Computer Science, 135, 510–517. https://doi.org/10.1016/j.procs.2018.08.203

Satria Putra, Iskandar Fitri, S. N. (2021). Absensi Pengenalan Wajah Menggunakan Menggunakan Algoritma Eigenface Berbasis Web. Journal of Applied Informatics and Computing (JAIC), 5(1), 21–27.

Wuryandari, M. D., & Afrianto, I. (2012). Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah. Komputa, 1(1), 45–51.

Yusvida Putri. (2019). Pengenalan ekspresi wajah menggunakan metode local binary pattern histogram dan jaringan syaraf tiruan”. Komputer, . J. S., Komputer, F. I., & & Sriwijaya.

Zafaruddin, G. M., & Fadewar, H. S. (2018). Face recognition using eigenfaces. Advances in Intelligent Systems and Computing, 810(5), 855–864. https://doi.org/10.1007/978-981-13-1513-8_87