Implementasi Metode Convolutional Neural Network untuk Deteksi Penggunaan Masker secara Real-Time - Marinda, Deva Ega; Amin, Imam Husni Al (LPPM UNIKA Santo Thomas, 2023)
The ongoing COVID-19 pandemic has led many countries to implement mask-wearing policies as a way to control the spread of the virus. Therefore, real-time mask detection can help in ensuring public compliance with the policy. This research uses a public dataset of 1300 masked and unmasked images. After that, a CNN network structure was developed using the Java programming language and the TensorFlow framework. The test results show that the developed system can detect the use of masks with an accuracy of 97.5% at camera distances from 1 to 10 meters. The test results show that factors such as the distance from the camera to the object, the presence of other obstructing objects, and the lack of lighting can affect the accuracy of the system. The results of this study show that the use of the Convolutional Neural Network method can be an effective solution for detecting mask use in real-time. This system can be used to help ensure public compliance with mask use policies and assist in controlling the spread of the COVID-19 virus.