Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)

Abstract

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%.