Classification of Face Mask Detection Using Transfer Learning Model DenseNet169 - Lidya Fankky Oktavia Putri; Ahmad Junjung Sudrajad; Vinna Rahmayanti Setyaning Nastiti (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022)
COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images