Development of a Face Mask Type Detection with Multiclass Classification Using Artificial Intelligence on Python

This research presents a system based on a machine learning system with artificial intelligence. The approach presented uses a camera to detect people who are not wearing face masks and it is hoped that it can detect type from mask. The purpose of this study is to be able to compare whether a computer system can correctly distinguish different types and types of masks using multiclass classification with artificial intelligence and training datasets on the system. And testing with different amounts of data does it affect the detection sensitivity of the system. The two types of masks have a large accuracy value of 99%, and when the program is run with a multi-category dataset of four types of masks has a lower accuracy model rate of 93%. The main contributions of this research are creating a prototype face mask detector which is implemented in three phases to help detect the presence of a face mask detector in real-time using images and video streams. The model created has used deep-learning methods to develop classifiers and collect photos of individuals wearing masks and not wearing masks. Mask and non-mask class. Which is then implemented in Python with Google Colab environment along with the Open-CV modules, Keras, NumPy, tensor flow, sci-py, and matplotlib. Trials will be carried out with the dataset training process first using CNN and then collecting the data system accuracy.