IMPLEMENTASI DEEP LEARNING TERHADAP PRESENSI MAHASISWA MENGGUNAKAN METODE MTCNN DAN FACENET (STUDI KASUS: KAMPUS ESA UNGGUL BEKASI)
Article Sidebar
student attendance,
deep learning,
MTCNN,
FaceNet
Main Article Content
Abstract
The development of digital technology has opened opportunities for educational institutions to improve the efficiency and accuracy of administrative systems, including student attendance recording. The current attendance system, which relies on RFID cards, often encounters issues such as damaged, lost, or unreadable cards, leading to long queues and the need for manual administration. This study aims to address these problems by developing an automatic attendance system based on facial recognition using deep learning technology. The proposed system integrates the Multi-task Cascaded Convolutional Neural Networks (MTCNN) algorithm for face detection and FaceNet for face recognition. Data collection is conducted by acquiring student facial images as the dataset for model training. The data is processed through normalization, face detection, and feature extraction using FaceNet embeddings. The system is integrated with a MySQL database to record student attendance in real time. Testing results show that the system performs well in detecting and recognizing student faces with satisfactory accuracy levels, despite variations in lighting conditions. By reducing dependency on physical cards, this system can streamline the attendance process and provide ease of use for users. This study demonstrates that the application of deep learning technology has the potential to improve the efficiency of attendance management in higher education institutions.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
A. Raup, W. Ridwan, Y. Khoeriyah, S. Supiana, and Q. Y. Zaqiah, “Deep Learning dan Penerapannya dalam Pembelajaran,” JIIP - Jurnal Ilmiah Ilmu Pendidikan, vol. 5, no. 9, pp. 3258–3267, 2022, doi: 10.54371/jiip.v5i9.805.
S. Wahyuni and M. Sulaeman, “Penerapan Algoritma Deep Learning Untuk Sistem Absensi Kehadiran Deteksi Wajah Di PT Karya Komponen Presisi,” Jurnal Informatika SIMANTIK, vol. 7, no. 1, pp. 5–6, 2022, [Online]. Available: https://simantik.panca-sakti.ac.id/index.php/simantik/article/view/127
K. G. Taruna and S. Budi, “Penerapan Data Science pada Dataset Olympics,” Jurnal Strategi, vol. 4, no. November, pp. 2443–2229, 2022.
M. R. S. Alfarizi, M. Z. Al-farish, M. Taufiqurrahman, G. Ardiansah, and M. Elgar, “Penggunaan Python Sebagai Bahasa Pemrograman untuk Machine Learning dan Deep Learning,” Karya Ilmiah Mahasiswa Bertauhid (KARIMAH TAUHID), vol. 2, no. 1, pp. 1–6, 2023.
R. Fiddiyansyah, S. F. Ana Wati, A. S. Fitri, F. H. Zidane, and N. R. Kuslaila, “Analisis Dan Perancangan Sistem Presensi Mahasiswa Berbasis Teknologi Pengenalan Wajah Di Fakultas Ilmu Komputer Upn Veteran Jawa Timur,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 1, 2023, doi: 10.23960/jitet.v11i1.2868.
Andri Nugraha Ramdhon and Fadly Febriya, “Penerapan Face Recognition Pada Sistem Presensi,” Journal of Applied Computer Science and Technology, vol. 2, no. 1, pp. 12–17, 2021, doi: 10.52158/jacost.v2i1.121.
M. Azamy, A. B. Ariwibowo, and I. Mardianto, “Face Recognition Implementation with MTCNN on Attendance System Prototype at Trisakti University,” Indonesian Journal of Banking and Financial Technology, vol. 1, no. 1, pp. 73–88, 2023.
F. M. Andiani and B. Soewito, “Face recognition for work attendance using multitask convolutional neural network (MTCNN) and pre-trained facenet,” ICIC Express Letters, vol. 15, no. 1, pp. 57–65, 2021, doi: 10.24507/icicel.15.01.57.
R. Khana, A. E. Saputra, and M. Sobirin, “Implementasi Sistem Presensi Deteksi Wajah Menggunakan YOLOv5,” vol. 1, no. Maret, pp. 54–66, 2024.
A. T. U. BR. Lubis, N. S. Harahap, S. Agustian, M. Irsyad, and I. Afrianty, “Question Answering System pada Chatbot Telegram Menggunakan Large Language Models (LLM) dan Langchain (Studi Kasus UU Kesehatan),” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 3, pp. 955–964, May 2024, doi: 10.57152/malcom.v4i3.1378.
F. R. A. Sutiyo, N. S. Harahap, S. Agustian, and R. M. Candra, “Implementasi Question Answering Berbasis Chatbot Telegram Pada Tafsir Al-Jalalain Menggunakan Langchain dan LLM,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 5, pp. 2464–2472, 2024, doi: 10.30865/klik.v4i5.1784.
M. A. Khair, P. Aldiyuda, N. Enjelina P, M. Z. Zukhrufa, and M. Adrezo, “Perancangan Sistem Absensi Mahasiswa Berbasis Face Recognition di Lingkungan UPN Veteran Jakarta,” Informatik : Jurnal Ilmu Komputer, vol. 20, no. 1, pp. 35–42, 2024, doi: 10.52958/iftk.v20i1.6696.
Andri Nugraha Ramdhon and Fadly Febriya, “Penerapan Face Recognition Pada Sistem Presensi,” Journal of Applied Computer Science and Technology, vol. 2, no. 1, pp. 12–17, 2021, doi: 10.52158/jacost.v2i1.121.
B. Santoso and R. P. Kristianto, “Implementasi Penggunaan Opencv Pada Face Recognition Untuk Sistem Presensi Perkuliahan Mahasiswa,” Sistemasi, vol. 9, no. 2, p. 352, 2020, doi: 10.32520/stmsi.v9i2.822.
A. S. Ningrum and A. W. Utami, “Sistem Deteksi Tulisan Tangan Aksara Hangeul Satu Silabel Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Framework Flask,” Journal of Emerging Information Systems and Business Intelligence, vol. 04, no. 04, pp. 9–16, 2023.
E. Pabianan and C. Dewi, “Perancangan Global Positioning System (Gps) Pada Sistem Presensi Online Berbasis Website Menggunakan Metode Waterfall,” Jurnal Pendidikan Teknologi Informasi (JUKANTI), vol. 6, no. 2, pp. 285–298, 2023, doi: 10.37792/jukanti.v6i2.1038.
A. Noviantoro, A. B. Silviana, R. R. Fitriani, and H. P. Permatasari, “Rancangan Dan Implementasi Aplikasi Sewa Lapangan Badminton Wilayah Depok Berbasis Web,” Jurnal Teknik dan Science, vol. 1, no. 2, pp. 88–103, 2022, doi: 10.56127/jts.v1i2.108.
M. F. Ajiz, M. F. S. Ramadan, H. D. Mutia, and P. D. Yanuari, “Pengembangan Aplikasi Chatbot Informasi Akademik Berbasis Web Menggunakan Metode Artificial Intelligence Markup Language (AIML),” Media Jurnal Informatika, vol. 15, no. 2, pp. 143–148, Dec. 2023, doi: 10.35194/mji.v15i2.3316.