Pengenalan Masker Wajah Menggunakan VGG-16 dan Multilayer Perceptron
DOI:
https://doi.org/10.61769/telematika.v17i2.519Keywords:
ReLu, Adam, VGG 16, multilayer perceptron, transfer learningAbstract
The use of face masks during the Covid-19 pandemic can be identified based on images taken of a person's face and then classified based on the results of their feature extraction. VGG 16 is a pre-trained CNN model that can extract 4,096 features from an image and transfer learning to the multilayer perceptron algorithm in classifying someone using a face mask. The results of this study indicate that the combination of ReLu activation with adaptive moment optimization (Adam) and stochastic gradient descent (SGD), the combination of ReLu and Adam, produces the best classification performance with accuracy, precision, and recall values of 98.1%.
References
C. K. Poetra, S. F. Pane, dan R. N. S. Fatonah, “Meningkatkan akurasi long-short term memory (LSTM) pada analisis sentimen vaksin Covid-19 di Twitter dengan Glove,” J. Telemat., vol. 16, no. 2, hlm. 85–90, 2021.
L. Martinelli, dkk., “Face masks during the Covid-19 pandemic: a simple protection tool with many meanings,” Front. Public Heal., vol. 8, Januari, hlm. 1-12, 2021.
F. Amer, M. Ali, dan M. S. H. Al-Tamimi, “Face mask detection methods and techniques: a review,” Int. J. Nonlinear Anal. Appl, vol. 13, Februari, hlm. 2008–6822, 2022.
G. NaliniPriya, M. Shobana, C. Siva, B. Kanisha, J. K. Monica, dan V. Siva Vadivu Ragavi, “Dynamic face mask detection using machine learning,” dalam 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Juni, 2022, hlm. 1-5.
M. F. Naufal dan S. F. Kusuma, “Pendeteksi citra masker wajah menggunakan CNN dan transfer learning,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 6, hlm. 1293, 2021.
P. R. Togatorop dan A. Fauzi, “Classification of face mask usage using Squeezenet,” Jatisi (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, hlm. 397–406, 2022.
R. Rismiyati dan A. Luthfiarta, “VGG-16 transfer learning architecture for salak fruit quality classification,” Telematika, vol. 18, no. 1, hlm. 37, 2021.
S. Tammina, “Transfer learning using VGG-16 with deep convolutional neural network for classifying images,” Int. J. Sci. Res. Publ., vol. 9, no. 10, hlm. 143-150, 2019.
M. Kurniawan, M. Hakimah, dan S. Agustini, “Perbandingan SVM dan perceptron dengan optimasi heuristik,” J. Telemat., vol. 15, no. 2, hlm. 85–92, 2020.
M. Handayani, M. Riandini, dan Z. Zakarias, “Comparison of neural network optimization functions in candidate husband eligibility classification,” J. Inform., vol. 9, no. 1, hlm. 78–84, 2022.
D. Pardede, B. H. Hayadi, dan Iskandar, “Multilayer perceptron literature review how well this algorithm performs,” J. ICT Apl. Syst., vol. 1, no. 1, hlm. 23–35, 2022.
E. Pirdia Wanti, “Pengidentifikasian citra ikan berformalin dengan menggunakan metode multilayer perceptron,” J. Sains Komput. Inform., vol. 5, no. 1, hlm. 491–502, 2021.
S. Shadkani, A. Abbaspour, S. Samadianfard, S. Hashemi, A. Mosavi, dan S. S. Band, “Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: the case study of the Mississippi River, U.S.,” Int. J. Sediment Res., vol. 36, no. 4, hlm. 512–523, 2021.
M. A. Firmansyah, K. N. Ramadhani, dan A. Arifianto, “Pengenalan angka tulisan tangan menggunakan diagonal feature extraction dan klasifikasi artificial neural network multilayer perceptron,” Indones. J. Comput., vol. 3, no. 1, hlm. 65–74, 2018.
J. Pardede, B. Sitohang, S. Akbar, dan M. L. Khodra, “Implementation of transfer learning using VGG-16 on fruit ripeness detection,” Int. J. Intell. Syst. Appl., vol. 13, no. 2, hlm. 52–61, 2021.
J. N. Mogan, C. P. Lee, K. M. Lim, dan K. S. Muthu, “VGG-16 - MLP: Gait recognition with fine-tuned VGG-16 and multilayer perceptron,” Appl. Sci., vol. 12, no. 15, hlm. 1–12, 2022.
Y. Gultom, A. M. Arymurthy, dan R. J. Masikome, “Batik classification using deep convolutional network transfer learning,” J. Ilmu Komput. dan Inf., vol. 11, no. 2, hlm. 189-194, 2018.
O. Gurav, “Face mask detection dataset,” 2020. [Daring]. Tersedia: https://www.kaggle.com/datasets/omkargurav/face-mask-dataset.
K. L. Kohsasih, M. Dipo, A. Rizky, T. Fahriyani, V. Wijaya, dan R. Rosnelly, “Analisis perbandingan algoritme convolutional neural network dan algoritme multilayer perceptron neural dalam klasifikasi citra sampah,” J. Times, vol. 10, no. 2, hlm. 22–28, 2022.
D. Irfan, R. Rosnelly, M. Wahyuni, J. T. Samudra, dan A. Rangga, “Comparison of SGD, Adadelta, and Adam optimization in hydrangea classification using CNN,” J. Sci. Soc. Res., vol. 5, no. 2, hlm. 244-253, 2022.
J. Kusuma, B. H. Hayadi, dan R. Rosnelly, “Comparison of multilayer perceptron (MLP) and support vector machine (SVM) methods for breast cancer classification,” MIND (Multimedia Artif. Intell. Netw. Database) J., vol. 7, no. 1, hlm. 51–60, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Khairul Fadhli Margolang, Sugeng Riyadi, Rika Rosnelly, Wanayumini
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
