Attendance System Face Recognition Using Convolutional Neural Network (CNN)
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Abstract
This article discusses the development of technology in various fields, with a focus on the implementation of digital technology and machine learning. Digitalization has influenced various aspects of life, including education and tourism. Machine learning, particularly convolutional neural networks (CNNs) and deep learning play an important role in these advancements, with applications extending from biology to healthcare. Face recognition technology, as part of biometrics, is highlighted in this article, used in various contexts such as security and enterprise management. This research implements CNN and Haar Cascade Classifier methods to build a face recognition system in the context of library attendance. With the tests conducted, the system achieved 95% accuracy, showing a good ability to detect faces in various conditions. In conclusion, the CNN algorithm can produce an effective face recognition system for use in library attendance systems, with reliable performance and high accuracy.
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References
Syamsuar and Reflianto, “PENDIDIKAN DAN TANTANGAN PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI DI ERA REVOLUSI INDUSTRI 4.0.”
T. Kurnialensya and P. C. Saputra, “Absensi SISTEM MONITORING KEHADIRAN SISWA MENGGUNAKAN MIKROKONTROLLER BERBASIS WEB,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 8, no. 1, pp. 92–99, Jan. 2023, doi: 10.36341/rabit.v8i1.3039.
P. Studi et al., “DIGITALISASI SISTEM ABSENSI UNTUK MONITORING KEGIATAN PEMBELAJARAN BERBASIS WEB RESPONSIVE I Nyoman Suraja Antarajaya 1) , Made Pradnyana Ambara 2).”
I. Zufria, R. A. Putri, and R. Ritonga, “PEMBANGUNAN SISTEM INFORMASI MANAJEMEN SEKOLAH BERBASIS WEB GUNA MENINGKATKAN EFEKTIFITAS PENGELOLAAN AKADEMIK DAN NON AKADEMIK PADA SMPN 1 PERCUT SEI TUAN,” JISTech (Journal of Islamic Science and Technology) JISTech, vol. 7, no. 1, pp. 53–64, [Online]. Available: http://jurnal.uinsu.ac.id/index.php/jistech
R. Mawarni and M. I. Fasa’, “Penerapan Digital Banking Bank Syariah Sebagai Upaya Customer Retantion Pada Masa Covid-19”.
Y. K. Sudira and R. Rachman, “APLIKASI TOUR GUIDE BERBASIS MOBILE MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY (STUDI KASUS KEBUN BINATANG BANDUNG),” 2021. [Online]. Available: https://jurnal.umj.ac.id/index.php/just- it/index
C. Fathul Hadi, R. Mustika Yasi, and C. Agustin, “Aplikasi Teknologi QR Code Pada Identifikasi Tumbuhan Di Wisata De-Djawatan,” TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat, vol. 2, no. 1, pp. 7–12, Apr. 2022, doi: 10.36526/tekiba.v2i1.1583.
A. Arif Bakti Nugraha and M. Rizcky Hikmawan, “Attendance System Implementation Existing Android Platform Using React Native Framework,” CoreID Journal, vol. 1, no. 2, pp. 83–89, Jul. 2023, doi: 10.60005/coreid.v1i2.9.
S. A. Friedler, S. Choudhary, C. Scheidegger, E. P. Hamilton, S. Venkatasubramanian, and D. Roth, “A comparative study of fairness-enhancing interventions in machine learning,” in FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, Inc, Jan. 2019, pp. 329–338. doi: 10.1145/3287560.3287589.
A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang,” Jurnal Khatulistiwa Informatika, vol. 5, no. 1, p. 490845, 2020.
A. A. Permana and D. Agustriawan, “Pengantar Bioinformatika untuk Memahami Riset dalam Bidang Komputasi Biologi,” 2023.
M. Feickert and B. Nachman, “A Living Review of Machine Learning for Particle Physics,” Feb. 2021, [Online]. Available: http://arxiv.org/abs/2102.02770
N. A. Widiastuti, S. Santosa, and C. Supriyanto, “Algoritma Klasifikasi data mining naïve bayes berbasis Particle Swarm Optimization untuk deteksi penyakit jantung,” Pseudocode, vol. 1, no. 1, pp. 11–14, 2014.
A. Fahri and Y. Ramdhani, “Visualisasi Data dan Penerapan Machine Learning Menggunakan Decision Tree Untuk Keputusan Layanan Kesehatan COVID-19,” Jurnal Tekno Kompak, vol. 17, no. 2, pp. 50–60, 2023.
R. G. Wardhana, G. Wang, and F. Sibuea, “Penerapan Machine Learning Dalam Prediksi Tingkat Kasus Penyakit Di Indonesia,” Journal of Information System Management (JOISM), vol. 5, no. 1, pp. 40–45, 2023.
R. Rachman, “Sistem Pakar Deteksi Penyakit Refraksi Mata Dengan Metode Teorema Bayes Berbasis Web,” Jurnal Informatika, vol. 7, no. 1, pp. 68–76, 2020.
M. R. A. Zayyad and A. Kurniawardhani, “Penerapan Metode Deep Learning pada Sistem Rekomendasi Film,” AUTOMATA, vol. 2, no. 1, 2021.
P. Sukabumi, R. Fawwaz Pradipta, D. Darlis, and S. Rangkuti, “Prosiding SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Face Recognition Sebagai Sistem Pendataan dan Akses Masuk Perpustakaan Daerah,” 2020.
N. Khan and M. Efthymiou, “The use of biometric technology at airports: The case of customs and border protection (CBP),” International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100049, 2021.
R. Muttaqin, Nopendri, S. Fuada, and E. Mulyana, “Attendance system using machine learning-based face detection for meeting room application,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, pp. 286–293, 2020, doi: 10.14569/IJACSA.2020.0110837.
R. Feng, J. Gu, Y. Qiao, and C. Dong, “Suppressing Model Overfitting for Image Super-Resolution Networks.”
S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun, “Object detection networks on convolutional feature maps,” IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 7, pp. 1476–1481, 2016.
K. Ivancic, “Traditional face detection with python,” Online) https://realpython. com/traditional- facedetection-python/. Diakses (26 Juli 2019), 2019.
S. Chau, J. Banjarnahor, D. Irfansyah, and S. Kumala, “Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature,” Journal of Informatics and Telecommunication Engineering, vol. 2, no. 2, pp. 69–76, 2019.
I. J. Tarigan, “Combination of Haar Cascade Classifier and Convolutional Neural Networks for Classification of Face Image,” 2022. [Online]. Available: http://ejournal.enlightenlearner.com