DESIGN AND IMPLEMENTATION OF EDGE COMPUTING SUBSYSTEM FOR EARLY DETECTION SYSTEM OF FEVERISH INFECTIOUS DISEASE IN PUBLIC AREAS - Ignacia Surya, Steven
After the global outbreak of the Covid-19 pandemic, human concerns about health and safety have increased. However, there are still individuals who knowingly or unknowingly carry infectious diseases and engage in activities on campus. This poses a risk of disease transmission within the campus area, affecting students, faculty, and staff, and hindering normal activities. Indirectly, those who are exposed or infected incur medical expenses, work or learning absences, and even hospitalization. Due to its limitations and potential inefficiencies, online learning has emerged as a necessary alternative for in-class instruction. Therefore, it is important to develop and implement strategies to address the spread of infectious diseases within the campus environment and ensure the continuity of educational and professional activities. Consequently, a screening system is required to enable early detection of infectious diseases, particularly those related to fever, in order to limit the spread of infectious diseases in public environments. Based on the above description, a solution is proposed in the form of an IoT system called "Early Detection System for Infectious Diseases in Public Areas". One component of this system is the utilization of edge computing. The task of edge computing is to perform suspect identification based on facial image and temperature data captured from sensors through MQTT Protocol. Once a suspect is detected, the data will be sent to the cloud through HTTPS protocol, and the user will receive a warning notification via WhatsApp if their temperature exceeds the normal limit. In this system, edge computing is implemented using Raspberry Pi 4B as the hardware. Edge computing performs face recognition using a CNN algorithm with the VGG16 architecture. Generally, functional testing for each function has been successfully conducted.