Perbandingan Kinerja Model Pre-Trained CNN (VGG16, RESNET, dan INCEPTIONV3) untuk Aplikasi Pengenalan Wajah pada Sistem Absensi Karyawan
DOI:
https://doi.org/10.35870/jimik.v5i3.925Keywords:
Facial Recognition, Convolutional Neural Network (CNN), Employee Attendance System, Model ComparisonAbstract
Face recognition has become a key technology in improving the efficiency and security of modern employee attendance systems. This study compares the performance of three pre-trained Convolutional Neural Network (CNN) models - VGG16, ResNet50, and InceptionV3 - in the context of face recognition for employee attendance systems. The study evaluated the accuracy, consistency, and generalization of the models on a dataset of employee faces, using prediction accuracy, confusion matrix, and classification report measurement methods. Results showed InceptionV3 performed best overall, with high consistency and confidence, achieving up to 99% accuracy on the test data. ResNet50 showed consistent performance in some cases but required further fine-tuning, while VGG16 showed the worst performance. These findings have significant practical implications for the industry, recommending the use of InceptionV3 for the implementation of reliable face recognition-based attendance systems, with consideration of the use of confidence thresholds to optimize accuracy. This research also highlights the importance of further optimization, including hyperparameter fine-tuning and more sophisticated data augmentation strategies, to improve system performance under various work environment conditions.
Downloads
References
Bimorogo, S. D., & Kusuma, G. P. (2020). A comparative study of pretrained convolutional neural network model to identify plant diseases on android mobile device. International Journal, 9(3).
Gwyn, T., Roy, K., & Atay, M. (2021). Face recognition using popular deep net architectures: A brief comparative study. Future Internet, 13(7), 164. DOI: https://doi.org/10.3390/fi13070164.
Hartiwi, Y., Rasywir, E., Pratama, Y., & Jusia, P. A. (2020). Sistem Manajemen Absensi dengan Fitur Pengenalan Wajah dan GPS Menggunakan YOLO pada Platform Android. Jurnal Media Informatika Budidarma, 4(4), 1235-1242. DOI: http://dx.doi.org/10.30865/mib.v4i4.2522.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Kamencay, P., Benco, M., Mizdos, T., & Radil, R. (2017). A new method for face recognition using convolutional neural network. Advances in Electrical and Electronic Engineering, 15(4), 663-672.
Kang, B. N., Kim, Y., & Kim, D. (2017). Deep convolutional neural network using triplets of faces, deep ensemble, and score-level fusion for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 109-116).
Ko, H., Chung, H., Kang, W. S., Kim, K. W., Shin, Y., Kang, S. J., ... & Lee, J. (2020). COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation. Journal of medical Internet research, 22(6), e19569.
Kumar, C. R., Saranya, N., Priyadharshini, M., & Gilchrist, D. (2023). Face recognition using CNN and siamese network. Measurement: Sensors, 27, 100800.
Lin, S., Chen, L., Chen, W., & Chang, B. (2024, January). Face Recognition via Thermal Imaging: A Comparative Study of Traditional and CNN-Based Approaches. In Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing (pp. 132-138). DOI: https://doi.org/10.1145/3647750.3647771.
Saragih, R. E., & To, Q. H. (2022). A survey of face recognition based on convolutional neural network. Indonesian Journal of Information Systems, 4(2).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. DOI: https://doi.org/10.48550/arXiv.1409.1556.
Sulistya, Y. I., Bangun, E. T. B., & Tyas, D. A. (2023). CNN Ensemble Learning Method for Transfer learning: A Review. ILKOM Jurnal Ilmiah, 15(1), 45-63.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
Wirayasa, I. K. A., Santoso, H., & Indrajit, E. (2021). Comparison of Convolutional Neural Networks Model Using Different Optimizers for Image Classification. International Journal of Sciences: Basic and Applied Research (IJSBAR), 60(2), 116-126.
Yaman, M. A., Rattay, F., & Subasi, A. (2021). Comparison of bagging and boosting ensemble machine learning methods for face recognition. Procedia Computer Science, 194, 202-209.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
