Sistem Pendeteksi Penggunaan Masker dengan Metode Convolutional Neural Network pada Sistem Portal Otomatis - Fran Fahlifi, Alwi; Heriansyah; Miranto, Afit (Jurusan Teknik Elektro Fakultas Teknik Universitas Bangka Belitung, 2021)
The very fast spreading process of COVID-19 has made this virus a pandemic in various countries. To reduce the spread of the COVID-19 virus, it is mandatory for everyone to follow health protocol rules such as social distancing and wearing masks. The health protocol examination is carried out by special personnel before entering the mandatory area to use a mask, which of course this examination will require more energy and cannot be done every time. In this study, a tool was made that could detect health protocols which would later reduce the workload of special workers. This tool can detect the use of masks on a person, which is made using the MobileNetV2 architecture and the Convolutional Neural Network (CNN) method that classifies people as not wearing masks and wearing masks. This tool uses the Raspberry Pi as a mini computer which is the main brain by adding a camera sensor to detect someone using a mask in real-time, RGB LEDs as a marker of whether the mask is detected or not, and the LCD as a display when the system is running. The effective distance that can detect the use of masks is as far as 30-200 cm.