Deteksi Orang Bermasker Medis Menggunakan Metode Convolutional Neural Network Berbasis Raspberry Pi - Bimo Dimas Nugraraga; Hurriyatul Fitriyah; Dahnial Syauqy (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
In the COVID - 19 pandemic, mask is an important commodities. The usage of masks is very important to prevent transmission of COVID - 19, especially in important institutions such as Hospital. Medical mask is very good to prevent transmission of COVID - 19 because medical mask has 3 protective layers. The Detection Of Medical Mask Using Raspberry Pi Based On Convolutional Neural Network aims to prevent the spread of COVID - 19 in hospitals by preventing people who do not use medical mask from entering hospitals. This system consist of a webcam, Raspberry Pi 4, and solenoid lock. Image processing is done by converting the color from RGB to YCbCr to detect medical masks and remove the background. This system use Convolutional Neural Network for classification method. The solenoid lock will open if the result of the classification is a medical mask and will be locked if the result of the classification is a non-medical. In this study, testing was carried out at 5 different distances, namely distances of 0.5 meters, 1.0 meters, 1.5 meters, 2.0 meters, 2.5 meters. Overall accuracy of this system is 97%. The average execution time of this system is 0.563271 seconds.