Development of a Student Attendance System Based on Face Recognition at SMKN 1 Luwu Utara
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Abstract
The face recognition-based attendance system is an innovative solution to improve the efficiency and accuracy of student attendance at SMKN 1 Luwu Utara. This study aims to develop and implement a Face Recognition-based attendance system using the Convolutional Neural Network (CNN) algorithm. The research method used is an experimental method with stages of needs analysis, system design, implementation, and system testing. The results show that this attendance system has an accuracy rate of up to 95% in recognizing students' faces, thus reducing the risk of attendance fraud. Additionally, the system provides a seamless and automated way of tracking attendance, eliminating manual errors and reducing administrative workload. The implementation of this system has also received positive responses from the school due to its ease of use and effectiveness in recording attendance data in real-time. The system's ability to integrate with existing school databases further enhances its practicality and usability
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