Simulasi Pemanfaatan Aplikasi Sistem Deteksi Pengguna Masker Pada Ruangan Laboratorium - Giri Wahyu Wiriasto; Islahul Anwar (Jurnal Ilmiah Pengabdian dan Inovasi, 2024)
During the post-pandemic or endemic period, the government continues to recommend the use of masks and maintenance of hygiene when engaging in activities outside the home. This extends to activities on the campus of the University of Mataram, particularly in the electrical engineering computer laboratory, where students are consistently encouraged to wear masks. To streamline activities in the laboratory, a Mask Detection System using the Haar Cascade Classifier (HCC) Method integrated with a Telegram Bot has been tested. The trial implementation aims to instill discipline among students in mask usage. This system was developed using Visual Studio Code and Python 3.10 tools, with image data input from a webcam. The HCC method is employed to detect students wearing masks or not in the laboratory, utilizing webcam-captured image data. Subsequently, the Telegram bot sends images if individuals are identified wearing masks. The study results demonstrate the system's capability to detect masks within a range of 30 cm to 120 cm. The warning feature, consisting of recorded images of students wearing masks sent via the Telegram bot, operates effectively.