Abstract
The use of masks is still very strict in public places, especially in hospitals, this is solely done to prevent the spread of the corona virus again. The purpose of this research is to assist examination workers or health protocol workers in supervising the use of masks in public places. Mask detection is a solution to this problem, by utilizing computer vision technology and applying supervised learning algorithms. For this mask detection classification method, this system uses the Naive Bayes method. The output of this mask detection system is planned to distinguish people wearing masks and not wearing masks, by giving red labels to people who are not wearing masks and green labeling to people wearing masks. The distance aspect is used in testing this mask detection system, the system is able to work well by getting an error rate presentation below 2% and getting the highest accuracy of 100% with an average percentage value of 98%. On the other hand, there are still weaknesses in this system, the use of brown masks that are in harmony with skin color can worsen the results of the classification system
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