Classification of Face Mask Detection Using Transfer Learning Model
DenseNet169

Classification of Face Mask Detection Using Transfer Learning Model
DenseNet169

classification, transfer learning, COVID-19, DenseNet169, face mask

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 in order 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, so as 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.

Lidya Fankky Oktavia Putri1
, Ahmad Junjung Sudrajad2
, Vinna Rahmayanti Setyaning Nastiti3

Muhammadiyah Malang

: 31-10-2022

Fajar Bagus W

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Indonesia

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