System of student attendance with IoT-based face identification using Raspberry Pi

Attendance is one way to control and monitor students while learning at school. It is important since attendance may also improve students learning. However, many schools still use manual ways to record student attendance. This research aims to develop an attendance system for students with face detection based on the Internet of Things (IoT). We use the OpenCV library embedded in Raspberry Pi to identify student faces, record, and place it. The algorithm for detecting faces uses Haar Cascade, while face recognition uses Local Binary Pattern Histograms (LBPH). The attendance records are then saved to MySQL database and the attendance reports are displayed on web browsers. The method employed in this research consists of several stages, namely Analysis, Design, Development, Implementation, and Evaluate (ADDIE). To evaluate the system, we use software and hardware system experts using black box tests to observe input and output performance and the resulting accuracy. Based on the test results, the function of the IoT-based student attendance system using raspberry pi has run as expected.