Open AccessShafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K – Nearest Neighbor
Author(s) -
Aripin Rambe,
Juliansyah Putra Tanjung,
Muhathir Muhathir
Publication year - 2022
Publication title -
jite (journal of informatics and telecommunication engineering)
Language(s) - English
Resource type - Journals
eISSN - 2549-6255
pISSN - 2549-6247
DOI - 10.31289/jite.v5i2.6214
Subject(s) - facial recognition system , principal component analysis , biometrics , pattern recognition (psychology) , artificial intelligence , face (sociological concept) , k nearest neighbors algorithm , computer science , feature (linguistics) , euclidean distance , word error rate , feature extraction , mathematics , social science , linguistics , philosophy , sociology
Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.