Sistem Absensi Berbasis Deteksi Wajah dengan Pendekatan Eksperimen
DOI:
https://doi.org/10.55123/insologi.v4i6.6700Keywords:
Face Detection, MTCNN, Haar Cascade, Accuracy Comparison, Attendance SystemAbstract
This study compares the accuracy of two face detection algorithms, Haar Cascade and Multi-task Cascaded Convolutional Networks (MTCNN), to determine the most suitable method for implementation in a facial recognition–based attendance system. The evaluation was conducted through a series of tests under common real-world conditions, including variations in distance, lighting intensity, and face orientation. Each algorithm was assessed using performance metrics such as Precision, Recall, F1-Score, and processing time to provide a comprehensive understanding of their strengths and limitations. The results indicate that MTCNN consistently achieves higher accuracy across nearly all tested scenarios, particularly under low-light conditions and when the face is not oriented frontally. In contrast, Haar Cascade demonstrates faster processing time but experiences significant decreases in accuracy under non-ideal conditions typically found in practical applications. Based on these findings, MTCNN is considered more suitable for attendance systems that require high accuracy and robustness to environmental variations, while Haar Cascade may be preferred in applications where computational efficiency and speed are the primary considerations.
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Anggara, K. D., Kartikasari, D. P., & Bakhtiar, F. A. (n.d.). Implementasi algoritma MTCNN dalam mekanisme autentikasi berbasis pengenalan wajah.
Bhandari, H. N. (2020, June 1). Confusion matrix, accuracy, precision, recall, F1 score. Medium. https://medium.com/analytics-vidhya/confusion-matrix-accuracy-precision-recall-f1-score-ade299cf63cd
Kagona, J. (2022). Face detection performance under variable lighting conditions: A case study at International University of East Africa. International Journal of Computer Vision Studies, 5(2), 45–53.
Kumari, P., & Kaur, G. (2023). Performance analysis of face detection algorithms using F1-Score evaluation metrics. International Journal of Artificial Intelligence Research, 8(1), 112–120.
Lavanya, A., Gaurav, L., Sindhuja, S., Seam, H., Joydeep, M., Uppalapati, V., Ali, W., & S.D, V. (2023). Assessing the performance of Python data visualization libraries: A review. International Journal of Computer Engineering in Research Trends, 10(1), 28–39. https://doi.org/10.22362/ijcert/2023/v10/i01/v10i0104
Majeed, M., Khan, R., & Ali, S. (2021). Comparison of Haar Cascade, DLIB, and MTCNN for real-time face detection. Journal of Computer Applications, 44(3), 77–85.
Meldyantono, A. P., & Poetro, B. S. W. (2025, Februari). Implementasi sistem absensi berbasis pengenalan wajah menggunakan metode CNN dan model FaceNet: Menggunakan metode CNN dan model FaceNet. Jurnal Rekayasa Sistem Informasi dan Teknologi, 2(3), 996–1006.
Rachmadi, R. F. (2020, May 15). Face detection dengan MTCNN – Reza Fuad Rachmadi Blog. https://notes.its.ac.id/fuad/2020/05/15/face-detection-dengan-MTCNN/
Rosid, J. (2022, Agustus). Face recognition dengan metode Haar Cascade dan FaceNet. Indonesian Journal of Data Science, 3(1). https://doi.org/10.56705/ijodas.v3i1.38
Salman, A. (2025). Implementation of MTCNN-based face recognition attendance system. Journal of Emerging Computing Technologies, 9(2), 134–142.
Silalahi, D. (2024). Aplikasi deteksi wajah berbasis Haar Cascade dengan Python. Jurnal Informatika dan Komputer, 13(1), 20–29.
Supiyandi, S., Ardiansyah, T., Balqis, S. P., Haqqoni, J., & Iskandar, S. N. (2024, November). Deteksi wajah dalam foto menggunakan teknologi visi komputer. Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer, 2(6), 32–39. https://doi.org/10.61132/mars.v2i6.490
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR).
Wang, Z., Cui, S., Wang, X., & Tian, J. (2022). Research on facial expression capture based on two-stage neural network. Computer Modeling in Engineering & Sciences, 72(3), 4709–4725. https://doi.org/10.32604/cmc.2022.027767
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multi-task cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499–1503.
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Copyright (c) 2025 Meryatul Husna, Kinarta Ketaren, Sharfina Faza , Orli Binta Tumanggor, Aprilza Aswani
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