Abstract
Since the outbreak of the COVID-19 virus, various technologies have developed as an alternative to preventing the spread of the COVID-19 virus; one of them is face mask detection. Many methods are used, such as Convolutional Neural Network, Haar cascade classifier, and more. This paper discusses how the system will work with face mask detection and the performance result while running the system against the parameters that can occur during training or direct testing by comparing several different methods. The test results display in the form of a line graph, and the Haar Cascade Classifier method will be displayed in tabular form, with the highest accuracy in the CNN method being 93%, while the Haar Cascade Classifier method is 96%
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