Klasifikasi Penggunaan Masker dengan Convolutional Neural Network Menggunakan Arsitektur MobileNetv2 - Ihsan Mudzakir; Toni Arifin (EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi, 2022)
In the current pandemic situation caused by the COVID-19 virus. The spread of the virus comes from droplets or splashes attached to objects or public facilities. The use of masks is an effort to minimize the spread of this virus. The use of masks is required when carrying out activities in public places that allow spread. However, there are still people who are ignorant of this, so there must be officers who carry out supervision to bring order to the community so that they always wear masks. Classification by using Deep Learning to detect the use of masks is one way to help bring order to the community and assist officers in conducting surveillance. In this research, the Deep Learning model is made using the Convolutional Neural Network method and the MobileNetv2 architecture. The selection of this method is based on the effectiveness of the method in resource utilization that is not too heavy and can produce maximum accuracy. The dataset used is sourced from an open. The results of the model test for real-time mask detection succeeded in detecting objects in 2 classes, namely, mask and without a mask, and displayed excellent and accurate results with an average value of 0.99 on precision, recall, f1-score, and support.