Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning
Classroom Attendance Based on Smiling Face Patterns and Nearby Wifi with Deep Learning
Abstract
Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.
Downloads
References
A. Fakih, I. K. Raharjana, and B. Zaman, “Pemanfaatan Teknologi Fingerprint Authentication untuk Otomatisasi Presensi Perkuliahan,” J. Inf. Syst. Eng. Bus. Intell., vol. 1, no. 2, p. 41, Nov. 2015, doi: 10.20473/jisebi.1.2.41-48.
T. Matsumoto and H. Matsumoto, “Impact of artificial gummy fingers on fingerprint systems,” Proc. …, 2002.
Samirso, “ZKTeco - How to Enter the Device Without Admin Affirming.” https://www.instructables.com/id/ZKTeco-How-to-Enter-the-Device-Without-Admin-Affir (accessed Aug. 17, 2019).
William and A. P. U. Sembiring, “Implementasi Identifikasi Sidik Jari Pada Sistem Informasi Penjualan,” Semin. Nas. Sist. Inf. Indones., vol. 2013, p. 351, 2013.
U. Syafitri, “Efektivitas Penerapan Absensi Finger Print Pada Lembaga Pendidikan Perkebunan Medan,” Univ. Medan Area, pp. 23–24, 2018.
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, p. 9, Apr. 2017, doi: 10.26418/jp.v3i1.19792.
Z. Fachmi, M. Sudarma, and L. Jasa, “Sistem Monitoring Kehadiran Perkuliahan Menggunakan Face Detection Dengan Algoritma Viola Jones,” Maj. Ilm. Teknol. Elektro, 2019, doi: 10.24843/mite.2019.v18i01.p18.
D. Derisma, “Sistem Pengenalan Wajah Secara Realtime Berbasis Android Menggunakan Metode Eigenface Pada OpenCV,” J. Komput. Terap., vol. 2, no. 2, pp. 127–136, 2016.
A. Pradana and E. Paulus, “Aplikasi Deteksi Wajah pada Sekumpulan Orang dengan Membandingkan Metode Viola-Jones dan KLT,” in SENAPATI 2016, 2016, pp. 230–231.
N. T. Son et al., “Implementing CCTV-based attendance taking support system using deep face recognition: A case study at FPT polytechnic college,” Symmetry (Basel)., 2020, doi: 10.3390/sym12020307.
M. A. Prasanty and F. Utaminingrum, “Sistem Presensi Mahasiswa Berdasarkan Pengenalan Wajah Menggunakan Metode LBP dan K-Nearest Neighbor Berbasis Mini PC,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. Vol. 4, no. April 2020, pp. 1168–1171, 2020.
G. Q. O. Pratamasunu, O. I. R. Farisi, and M. Jannah, “Pengenalan Wajah Mahasiswa Universitas Nurul Jadid Pada Video Menggunakan Metode Haar Cascade Dan Deep Learning,” Core-IT J. Komputasi dan Teknol. Inf., vol. Vol. 1, no. November 2020, pp. 25–34, 2020.
T. Stat, “Face Recognition Using,” vol. 4, no. 2. pp. 2–5, 2003, [Online]. Available: https://github.com/davidsandberg/facenet.
F. Hu, G. S. Xia, J. Hu, and L. Zhang, “Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery,” Remote Sens., 2015, doi: 10.3390/rs71114680.
E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” 2016, doi: 10.1109/IGARSS.2016.7730322.
J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” 2014.
JavaTpoint, “Convolutional Neural Network In PyTorch.” https://www.javatpoint.com/pytorch-convolutional-neural-network.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” 2015, doi: 10.1109/CVPR.2015.7298682.
A. Zein, “Pendeteksian Kantuk Secara Real Time Menggunakan Pustaka OpenCV dan DLIB Python,” Sainstech, 2018.
N. Wahyudiana and S. Budi, “Perbandingan Performa Pre-Trained Classifier dLib dan HAAR Cascade (OpenCV) Untuk Mendeteksi Wajah,” J. Strateg., vol. 1, p. 376, 2019.
N. Ramadhani, J. Hendryli, and D. E. Herwindianti, “Pencarian Objek Wisata Bersejarah Di Pulau Jawa Menggunakan Convolutional Neural Network,” J. Ilmu Komput. dan Sist. Inf., vol. 7, 2019.
Copyright (c) 2021 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
