Studi Literatur Metode Pengenalan Wajah untuk Presensi Siswa

Literature Study of Face Recognition Methods for Student Presence

Face recognition is a manual way that teachers do in the student presence process. Generally, the presence process is still done conventionally. This method is less efficient and has an impact on the learning process. It can also trigger student cheating. The purpose of this study is to find out and compare various methods that can be used as a means of making facial recognition systems for the presence of students, as well as knowing the results of image accuracy from each method. This study uses the Systematic Literature Review (SLR) method by identifying and evaluating previous studies in order to answer predetermined research questions. Based on the results of studies that have been carried out using 9 references, 5 types of face recognition methods were obtained, namely Three Level Wavelet Decomposition - Principal Component Analysis (3WPCA), Convolutional Neural Network - Principal Component Analysis (CNN-PCA), Haar-Cascade Classifier, Local Binary Pattern (LBP), and Eigenface. The best accuracy results in the face recognition process for student presence is the Local Binary Pattern Method combined with the Cascade Classifier Method with an accuracy percentage of 99%. By combining various methods will make the final result of face recognition during the student's presence process better.