Abstract
The World Health Organization (WHO) provides standard recommendations for the prevention and spread of COVID-19 with the importance of face masks for protection from the virus. Technology can be used to help prevent viruses from spreading quickly through direct contact. Utilizing Internet of Things (IoT) based technology tools for public place entrances can be designed to perform rapid inspections. This can be done using image classification methods, namely Haar Cascade and Convolutional Neural Network (CNN). This study aims to detect masks on the face using the python programming language and input data via a webcam camera. A total of 500 datasets of photos of faces without masks and 500 datasets of photos of faces with masks were used as training data. The test results using the Haar Cascade method have an accuracy of 80% while using CNN the accuracy is 100%. So that this study becomes a reference in identifying obedience in wearing masks.
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