Online Attendance with Python Face Recognition and Django Framework

Abstract

Online learning certainly requires an attendance system that is accessed anywhere with a minimum level of fraud. This research aimed to build an online attendance system using face recognition to prevent filling out online learning attendance represented by others. The online attendance system was built using the object-based system approach method. The system development method used was the waterfall. The development of this system utilizes the Django python framework, face recognition library, and OpenCV. This research delivered an attendance system that could not be represented by others. To record attendance, students visit the online attendance system. Students can only record attendance once according to the lecture schedule. The camera will capture the student’s face and equate it with the existing facial data. Only the registered student that his attendance data stored in the database. In addition, students cannot record attendance with face recognition outside of their lecture hours. This attendance system was tested using black-box testing. The test is carried out on the access button function to record attendance during the lecture schedules data and outside the lecture schedule, facial recognition function with valid and not valid facial data, function to store attendance data, and function to view attendance data recap in the current semester. The result showed that the attendance application with facial recognition is 100% running as it should and as expected.