Comparative Analysis of Convolutional Neural Network Methods in Detecting Mask Wear - Handoko Handoko; Fahrul Rozy; Felix Elbert Gani; Abdi Dharma (Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 2022)
Covid19 is a disease caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). As a result, the respiratory system becomes disrupted. The spread of this disease is very easy through droplets. The use of masks is one way to prevent being attacked by this virus. The system for detecting the use of masks is very necessary for today in detecting whether someone is wearing a mask. Convolutional Neural Network (CNN) is a method that can be used to detect masks. In this study, the VGG16, Resnet50, and MobileNet models will be used. Before conducting data training, data preprocessing and data augmentation were carried out on the dataset. The test accuracy of the VGG16, Resnet50, and MobileNet models are 96% and 96% and 98%, respectively. From the test results, it is found that the MobileNet model is more appropriate in the case of mask detection. The conclusion obtained is the use of the MobileNet architecture, the resources used for classifying can be reduced compared to other architectures. MobileNet uses the Depth-Wise Separable method in the computing process which reduces the computational process.