DESIGN OF AI-BASED CADET ATTENDANCE DETECTION CAMERA (ARTIFICIAL INTELLIGENCE) WITH THE FISHERFACE METHOD IN THE SURABAYA AVIATION POLYTECHNIC LABORATORY

Abstract

It is necessary to increase discipline for cadets to avoid indiscipline. Starting from the smallest thing, namely the attendance list of cadets, which requires development in attendance by using face detection as an effort to increase cadet discipline. Using face detection makes it easier for the study program to monitor the attendance of cadets. In addition, face detection ensures from the act of cheating attendance. This research aims to design and build an Artificial Intelligence (AI)-based cadet attendance detection camera system using the Fisherface method. The Fisherface method was chosen because of its ability to recognize faces with high accuracy despite variations in facial expressions, lighting, and distance. The system consists of several main components, namely the camera as a face image capture tool, and software that implements the Fisherface algorithm for face recognition. The result of this research is that the cadet attendance process at the Aviation Polytechnic of Surabaya can be done automatically and in real-time, reducing manual errors and increasing administrative efficiency. This system can be applied in the Surabaya Aviation Polytechnic Laboratory and study program rooms to simplify the attendance process and ensure the attendance of cadets in a more timely manner. The advantage of monitoring the attendance of cadets from previous research is a longer distance of 2 meters at lumen 239 and 2.5 meters at lumen 279. The speed in detecting and sending data to the database is much faster at 1.40 seconds. The accuracy (similarity rate) in detecting is 92.7% of 69 experimental data