Rancang Bangung Sistem Absensi Guru Berbasis Webcam Menggunakan Haar Cascade dan Local Binary patterns Histogram untuk Pengenalan Wajah
TL;DR
The system created can improve the management of teacher absences by using facial recognition technology and can detect and recognize teachers' faces automatically, so that recording attendance is faster, more precise and efficient compared to the usual method.
Abstract
The teacher attendance system, which is still done manually, has many shortcomings, such as the ease with which data can be changed, errors when recording, and the slow process of calculating attendance. This research aims to design and create a teacher attendance system that uses a webcam with the Haar Cascade method and Local Binary Pattern Histogram (LBPH) to recognize faces. The Haar Cascade method is used to detect faces directly, while LBPH is used to recognize who the teacher is based on facial characteristics that have been stored in the data. This system was created using the Python programming language to process images and the Laravel framework as a web application platform. Existing features include teacher data management, facial data management, attendance using facial recognition, permit applications, absenteeism calculations, and attendance reports. The results of the system show that it can detect and recognize teachers' faces automatically, so that recording attendance is faster, more precise and efficient compared to the usual method. In addition, attendance data can be stored in one place and displayed in the form of statistics and reports that are helpful in school administration. In this way, the system created can improve the management of teacher absences by using facial recognition technology.