Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)
Dublin Core
Title
Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)
Subject
Classification; PCA; SVM
Description
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%.
Creator
Intan Setiawati, Enny Itje Sela
Source
www.ijcit.com
Date
February 2022
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Intan Setiawati, Enny Itje Sela, “Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM),” Repository Horizon University Indonesia, accessed June 1, 2025, https://repository.horizon.ac.id/items/show/9018.