Implementation of Backpropagation Method with MLPClassifier to Face Mask Detection Model - Wendy Hendra Wijaya; Raymond Sunardi Oetama; Fransiscus Ati Halim (IJNMT (International Journal of New Media Technology), 2022)
Corona Virus Disease 2019 (COVID-19) is a virus that has spread widely and has become a global pandemic. The virus also can be spread through droplets made from coughs or sneezes. The Minister of Health of the Republic of Indonesia has issued a decision regarding this COVID-19 pandemic case, one of which is "Using personal protective equipment in the form of a mask that covers the nose and mouth to the chin”. This research aim is to detect masks on the face using the CRISP-DM framework and the backpropagation neural network method with MLPClassifier. The dataset is using RMFD (Real-World Masked Face Dataset. The dataset contains photos of human faces using mask and human faces without using mask. The result showed that the backpropagation neural network method can be used to detect mask on human faces with 94.4% accuracy. The accuracy from this research is outperform DNN algorithm. This research is expected to broaden the insight regarding the detection of masks to prevent the spread of COVID-19.