Classification of Face Mask Detection Using Transfer Learning Model DenseNet169
Abstract
COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images
Downloads
References
Melly Damara Chaniago, Amellia Amanullah Sugiharto, Qhistina Dyah Khatulistiwa, Zamah Sari, and Agus Eko, “Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 2, pp. 190–197, 2022, doi: 10.29207/resti.v6i2.3823.
R. Wihandika, “Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 705–712, 2021, doi: 10.29207/resti.v5i4.3094.
M. Ikbal, S. Andryana, and R. T. Komala Sari, “Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 5, no. 4, p. 389, 2021, doi: 10.35870/jtik.v5i4.233.
C. Wang et al., “The Association Between Physical and Mental Health and Face Mask Use During the COVID-19 Pandemic: A Comparison of Two Countries With Different Views and Practices,” Front. Psychiatry, vol. 11, no. September, pp. 1–13, 2020, doi: 10.3389/fpsyt.2020.569981.
J. T. Atmojo et al., “Penggunaan Masker Dalam Pencegahan Dan Penanganan Covid-19: Rasionalitas, Efektivitas, Dan Isu Terkini,” Avicenna J. Heal. Res., vol. 3, no. 2, pp. 84–95, 2020, doi: 10.36419/avicenna.v3i2.420.
P. P. Siregar, R. Sutan, and C. Mourisa, “COVID-19 dan Penggunaan Masker Muka: Antara Manfaat dan Resiko,” J. Implementa Husada, vol. 1, no. 3, pp. 221–231, 2020.
N. Audebert, B. Le Saux, and S. Lefevre, “Deep learning for classification of hyperspectral data: A comparative review,” IEEE Geosci. Remote Sens. Mag., vol. 7, no. 2, pp. 159–173, 2019, doi: 10.1109/MGRS.2019.2912563.
A. TiaraSari and E. Haryatmi, “Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 265–271, 2021, doi: 10.29207/resti.v5i2.3040.
R. Prathivi, “Optimasi Model TL-CNN Untuk Klasifikasi Citra CIFAR-10,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 3, pp. 717–722, 2017.
Luqman Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379–385, 2021, doi: 10.29207/resti.v5i2.3001.
S. Kornblith, J. Shlens, and Q. V. Le, “Do better imagenet models transfer better?,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 2656–2666, 2019, doi: 10.1109/CVPR.2019.00277.
W. Setiawan, M. I. Utoyo, and R. Rulaningtyas, “Transfer learning with multiple pre-trained network for fundus classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 3, pp. 1382–1388, 2020, doi: 10.12928/TELKOMNIKA.v18i3.14868.
A. Oumina, N. El Makhfi, and M. Hamdi, “Control the COVID-19 Pandemic: Face Mask Detection Using Transfer Learning,” 2020 IEEE 2nd Int. Conf. Electron. Control. Optim. Comput. Sci. ICECOCS 2020, 2020, doi: 10.1109/ICECOCS50124.2020.9314511.
M. F. Naufal and S. F. Kusuma, “Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 6, 2021, doi: 10.25126/jtiik.2021865201.
F. J. P. Montalbo and A. A. Hernandez, “Classification of fish species with augmented data using deep convolutional neural network,” 2019 IEEE 9th Int. Conf. Syst. Eng. Technol. ICSET 2019 - Proceeding, no. December, pp. 396–401, 2019, doi: 10.1109/ICSEngT.2019.8906433.
Y. Achmad, R. C. Wihandika, and C. Dewi, “Klasifikasi emosi berdasarkan ciri wajah wenggunakan convolutional neural network,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 11, pp. 10595–10604, 2019.
C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst. Eng., vol. 194, pp. 112–120, 2020, doi: 10.1016/j.biosystemseng.2020.03.020.
. Usha Ruby Dr.A, “Binary cross entropy with deep learning technique for Image classification,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5393–5397, 2020, doi: 10.30534/ijatcse/2020/175942020.
Z. Ren, Y. Zhang, and S. Wang, “A Hybrid Framework for Lung Cancer Classification,” Electronics, vol. 11, no. 10, p. 1614, 2022, doi: 10.3390/electronics11101614.
J. Dodge, G. Ilharco, R. Schwartz, A. Farhadi, H. Hajishirzi, and N. Smith, “Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping,” 2020, [Online]. Available: http://arxiv.org/abs/2002.06305
Copyright (c) 2022 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
