Classification of Proper or Improper Masks during a Pandemic Using a Custom Convolutional Neural Network with Data Augmentation

ABSTRACT

Classification of the use of masks today is very necessary regarding the pandemic period that is not over yet. This is mainly to break the chain of transmission of the COVID-19 virus from one person to another. From the literature that has been studied, the Convolutional neural network method can be used to distinguish the types of masks used in society. The advantage of the Convolutional Neural Network is that it can recognize objects with a fairly high level of accuracy, but it has a weakness, namely that the training process time is still quite high. This is the author's concern by doing a custom layer on the convolutional neural network. In addition, the addition of data augmentation is done to increase the number of data variations. The result used 18-34 custom layers in an average of around 97.93%, with an average computation time for the training process of about 1 minute 83 seconds. The resulting classification errors using Mean Absolute Error is 0,0163 © 2021 IEEE.