Detection of Facial Mask Using Deep Learning Classification Algorithm

Abstract

Deep learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in deep learning means that deep learning begins layers of sequential representation. This study aims to provide a reference on how to create a system and analyze the results of identifying face masks using a deep learning algorithm. Research on facemask detection is highly important as it tackles a vital element of public health and safety. It plays a crucial role in promoting adherence to mask-wearing guidelines, minimizing the transmission of infectious diseases, and offering valuable data for monitoring and policy assessment. Additionally, this area of study has garnered increased significance and attention within the realm of public health and safety, especially in light of the COVID-19 pandemic since 2020, where mask usage has been universally advised to protect individuals from the spread of the virus. From the results of the research conducted, it is known that this model can recognize faces well, both those who wear masks and those who do not use masks. This is evident from the average specificity and precision of 96.00% and the average sensitivity or recall value of 93.47%. In addition, this model has also proven to be quite accurate in conducting overall classification with an average accuracy of 94.73%. Received: 13 May 2023 | Revised: 7 July 2023 | Accepted: 19 July 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.