Open AccessClassification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)
Author(s) -
Intan Setiawati,
Enny Itje Sela
Publication year - 2022
Publication title -
international journal of computer and information technology
Language(s) - English
Resource type - Journals
ISSN - 2279-0764
DOI - 10.24203/ijcit.v11i1.205
Subject(s) - principal component analysis , support vector machine , pattern recognition (psychology) , artificial intelligence , computer science , machine learning
Classification is a process to assert an object into one of defined categories. This study examines the classification of recognition of student’s facial expression during digital learning –indifferent and serious expression. The dataset used was from a vocational school -SMK Muhammadiyah 2 Bantul. This study used the combination of algorithm: Principal Component Analysis (PCA) and Support Vector Machine (SVM) to increase the accuracy. This study aims at comparing the performance of combination of two algorithm: (PCA to SVM) and (PCA to k-NN). The result states that the combination of PCA-SVM algorithm is higher than the combination of PCA-k-NN algorithm with the average accuracy of 96% and 89%.