Implementation of Face Mask Detection Using Phyton Programming Language


    Yo Ceng Giap( 1 ) Erviana( 2 )

    (1) Universitas Buddhi Dharma
    (2) Universitas Buddhi Dharma



Computer vision, Face mask, Masks, Phyton, Yolo


Since the beginning of the pandemic in 2019, people in Indonesia have been required to wear masks. Although until now the pandemic has ended, the need to use masks is still very much needed such as to maintain health, avoid air pollution and others. In detecting mask users, an application is needed that can help human work. Currently, the Python programming language is widely used to build applications in the field of computer vision, one of which is this face mask detection application. This application will detect mask users, whether they are wearing a mask or not. This developed application uses the Yolo model using the Face Mask Detection dataset developed by Larxel, where the Yolo model can work on the dataset provided. The test results show that the Yolo model can recognize mask users with an accuracy value above 90%. The second experiment was carried out to detect several faces of mask users, the Yolo model can recognize mask users or not with an average accuracy value of 91.75%. For future research, it is also expected to use other models besides Yolo and make comparisons of several models and make improvements to the problems that exist in each model and use real time data.


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How to Cite

Y. Ceng Giap and Erviana, “Implementation of Face Mask Detection Using Phyton Programming Language”, bit-Tech, vol. 6, no. 1, pp. 51–58, Aug. 2023.



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