IMPLEMENTASI SISTEM ABSENSI BERBASIS PENGENALAN WAJAH MENGGUNAKAN METODE CNN DAN MODEL FACENET
Menggunakan Metode CNN dan Model FaceNet
DOI:
https://doi.org/10.70248/jrsit.v2i3.1857Keywords:
Attendance System, face recognition, Convolutional Neural Networks, FaceNet, Local Binary Patterns HistogramAbstract
This research implements a face recognition-based attendance system using Convolutional Neural Networks method and FaceNet model. This topic was chosen because face recognition is an effective identification method for attendance applications, but often faces challenges of low illumination and varying object distances, especially on devices with mid-to-low specifications. This system uses Convolutional Neural Networks for facial feature extraction, FaceNet to improve face representation accuracy, and Local Binary Patterns Histogram to analyze facial texture to improve recognition performance. The steps taken include collecting face datasets, applying Convolutional Neural Networks and FaceNet models, and evaluating the system under low lighting conditions and various object distances. The test results showed 100% accuracy with three face images even in low lighting conditions. The system still performs well despite variations in light intensity and object distance. The main contribution of this research is the development of an efficient face recognition system based on Convolutional Neural Networks and FaceNet that can be applied to devices with limited specifications for attendance applications, with a focus on stability in poor lighting and testing in real environments.
References
M. Khatama Insani and D. Budi Santoso, “Perbandingan Kinerja Model Pre-Trained CNN (VGG16, RESNET, dan INCEPTIONV3) untuk Aplikasi Pengenalan Wajah pada Sistem Absensi Karyawan,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK), vol. 5, no. 3, 2024, [Online]. Available: https://journal.stmiki.ac.id
N. M. Hussain Hassan, M. A. Moussa, and M. H. M. Mahmoud, “CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 1, pp. 133–139, Jul. 2024, doi: 10.11591/ijeecs.v35.i1.pp133-139.
S. Zahra, Azhar, and A. Musta’inul, “Syifa2022 Sistem Deteksi Wajah Untuk Pencatatan Kehadiran Mahasiswa Di Kls Menggunakan Metode CNN,” ournal of Artificial Intelligence and Software Engineering, vol. 2, no. 1, pp. 24–28, 2022.
Evelyn, R. Adipranata, and K. Gunadi, “Sistem Presensi Mahasiswa Menggunakan Face Recognition Dengan Metode Facenet Pada Android,” Jurnal Infra, vol. 10, no. 2, pp. 56–62, 2022.
D. Mantara Sakti, W. Sudoro Murti, A. Kurniasari, and J. Rosid, “Face recognition dengan metode Haar Cascade dan Facenet,” Indonesian Journal of Data and Science (IJODAS), vol. 3, no. 1, pp. 30–34, 2022.
S. Huang and H. Luo, “Attendance System Based on Dynamic Face Recognition,” Proceedings - 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020, pp. 368–371, Jul. 2020, doi: 10.1109/CISCE50729.2020.00081.
U. Essien and G. Ansa, “A deep learning-based face recognition attendance system,” Global Journal of Engineering and Technology Advances, vol. 17, no. 1, pp. 009–022, Oct. 2023, doi: 10.30574/gjeta.2023.17.1.0165.
D. Bathinko, G. Putra Kelana, and R. Meimaharani, “Perancangan Sistem Absensi Karyawan Berbasis Web Dengan Metode Waterfall,” 2024.
A. Muharman, A. Frinaldi, L. Magriasti, and F. Vena Anisa, “Implementasi Kebijakan Sistem Absensi Online Dalam Meningkatkan Kinerja Asn Di Bappeda Kota Sungai Penuh,” 2023. [Online]. Available: https://siabon.sungaipenuhkota.go.id
M. Saied and A. Syafii, “Perancangan dan Implementasi Sistem Absensi Berbasis Teknologi Terkini Untuk Meningkatkan Efisiensi Pengelolaan Kehadiran Karyawan dalam Perusahaan,” Jurnal Teknik Indonesia, vol. 2, no. 3, pp. 87–92, Jul. 2023, doi: 10.58860/jti.v2i3.21.
S. Kakarla, P. Gangula, M. S. Rahul, C. S. Singh, and T. H. Sarma, “Proceedings of 2020 IEEE-HYDCON, International Conference on Engineering in the 4th Industrial Revolution : September 11-12, 2020,” 2020.
R. B. Setiawan and N. Lukman, “Attendance System Face Recognition using Convolutional Neural Network (CNN),” CoreID Journal, vol. 1, no. 3, Mar. 2024, doi: 10.60005/coreid.v1i3.16.
V. Santosh, M. Panbude, K. P. Rao, G. Bhakt, M. Akanksha, and M. Student, “Attendance System Using Face Recognition and Detection,” International Journal of Scientific Research in Engineering and Management, 2024, doi: 10.55041/IJSREM33713.
F. Ramadhan, A. Triayudi, and E. Mardiani, “Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm,” SaNa: Journal of Blockchain, NFTs and Metaverse Technology, vol. 2, no. 2, pp. 123–130, Sep. 2024, doi: 10.58905/sana.v2i2.275.
A. Anggreini, “Pengembangan Sistem Presensi Karyawan dengan Face Recognition menggunakan Model Facenet,” Yogyakarta, 2024. Accessed: Nov. 20, 2024. [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/51600/20523151.pdf?sequence=1&isAllowed=y
R. V. Talumepa, D. A. Putra, and H. Soetanto, “Sistem Presensi Pendeteksi Wajah menggunakan Metode Modified Region Convolutional Neural Network dan PCA,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 1, pp. 46–55, Jun. 2024, doi: 10.29408/edumatic.v8i1.25207.
A. W. Murdiyanto, D. H. Zulfikar, B. S. Waluyo Poetro, and A. C. Siregar, “Performance Comparison of CNN and ResNet50 for Skin Cancer Classification Using U-Net Segmented Images,” Indonesian Journal of Data and Science, vol. 5, no. 3, pp. 198–205, Dec. 2024, doi: 10.56705/ijodas.v5i3.200.
B. S. W. Poetro, S. Mulyono, and V. A. Pramesti, “Prediksi Penyakit Batu Ginjal dengan Menerapkan Convolutional Neural Network,” Jurnal Buana Informatika, vol. 15, no. 2, pp. 153–162, 2024.
F. Cahyono, “Pengenalan Wajah Menggunakan Model Facenet Untuk Presensi Pegawai,” 2020. Accessed: Nov. 20, 2024. [Online]. Available: https://repository.its.ac.id/76660/2/07111850067013-Master_Thesis.pdf
O. Pribadi, “Aplikasi Pengenalan Wajah Menggunakan Algoritma Haar Cascade Classifier Dan Local Binary Pattern Histogram,” Jurnal TIMES, vol. 12, no. 1, pp. 40–47, 2023.
