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JOIG 2022 Vol.10(3): 122-126
doi: 10.18178/joig.10.3.122-126

A Comparative Analysis of Machine Learning Algorithms for Autonomous Face Mask Detection

Destin Joanny, Erik Sanjaya, Ronaldo, and Derwin Suhartono
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia

Abstract—Covid-19 pandemic is a global disease caused by severe acute respiratory syndrome. The rising number of infected individuals and death cases remain a major problem in 2021. Health protocols such as wearing a face mask is taken as prevention method to suppress the significantly growing numbers. Popular machine learning techniques have been addressed to assist in the global issue. This study intends to compare several popular classification algorithms namely K-Nearest Neighbors (K-Nearest Neighbors), Support Vector Machine (Support Vector Machine), Convolutional Neural Network (CNN), Decision Tree, and Naive Bayes for autonomous face mask detection as there are still limited sources of studies that does performance comparison of the related field. Experimental results are analyzed and evaluated using various measures such as precision, recall, accuracy, and F1 Score. Convolutional Neural Network proves to have the most promising performance than the other classification techniques to identify whether a person is wearing a mask or not with over 97% of accuracy.

Index Terms—Covid-19, autonomous face mask detection, classification algorithm

Cite: Destin Joanny, Erik Sanjaya, Ronaldo, and Derwin Suhartono, "A Comparative Analysis of Machine Learning Algorithms for Autonomous Face Mask Detection," Journal of Image and Graphics, Vol. 10, No. 3, pp. 122-126, September 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.