Pendeteksi Citra Masker Wajah Menggunakan CNN Dan Transfer Learning

Naufal, Mohammad Farid and Kusuma, Selvia Ferdiana (2021) Pendeteksi Citra Masker Wajah Menggunakan CNN Dan Transfer Learning. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 8 (6). pp. 1293-1300. ISSN 2355-7699; E-ISSN 2528-6579

PDF Mohammad Farid Naufal_PENDETEKSI CITRA MASKER WAJAH_Rev.pdf Download (1MB)

Abstract

In 2021 the Covid-19 pandemic is still a problem in the world. Therefore, health protocols are needed to prevent the spread of Covid-19. The use of face masks is one of the commonly used health protocols. However, manually checking to detect faces that are not wearing masks is a long and tiring job. Computer vision is a branch of computer science that can be used for image classification. Convolutional Neural Network (CNN) is a deep learning algorithm that has good performance in image classification. Transfer learning is the latest method to speed up CNN training and get better classification performance. This study performs facial image classification to distinguish people using masks or not by using CNN and Transfer Learning. The CNN architecture used in this research is MobileNetV2, VGG16, DenseNet201, and Xception. Based on the results of trials using 5-cross validation, Xception has the best accuracy of 0.988 with a total computation time of training and testing of 18274 seconds. MobileNetV2 has the fastest total computing time of 4081 seconds with an accuracy of 0.981.

Item Type:	Article
Uncontrolled Keywords:	face mask classification, CNN, Transfer Learning, MobileNetV2, VGG16, DenseNet201, Xception
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	MOHAMMAD FARID NAUFAL
Date Deposited:	24 Nov 2021 04:45
Last Modified:	11 Oct 2024 03:53
URI:	http://repository.ubaya.ac.id/id/eprint/40736

Actions (login required)

View Item