Convolutional Neural Network and Haversine Formula in Presence System for Easy Attendance

As COVID-19 cases continue to rise, minimizing physical contact is essential to curb the virus’s spread. IDE LPKIA, an educational institution, currently uses a centralized attendance system based on fingerprint scanning, which increases the physical contact and thus the potential for virus transmission. To address this issue, this research proposes a new attendance system that allows employees to mark their attendance independently using their personal smartphones, eliminating the need for centralized attendance stations. The proposed system integrates facial recognition and location radius technology. Facial recognition is implemented using a convolutional neural network (CNN) to ensure accurate identification, while the Haversine formula is employed to calculate the location radius, ensuring attendance can only be registered within a specific geographic area around the institution. This approach not only reduces physical contact but also prevents attendance fraud, as employees can only check in based on their facial identity and within the defined location radius. This system aims to enhance safety and integrity in attendance tracking amidst the ongoing pandemic.