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COVID-19 X-Ray Images Classification using Support Vector Machine and K-Nearest Neighbor

Journal
Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
Date Issued
2022-01-01
Author(s)
Jusman Y.
Mubarok D.W.
Riyadi S.
Kanafiah S.N.A.M.
DOI
10.1109/ICITACEE55701.2022.9924124
Handle (URI)
https://hdl.handle.net/20.500.14170/8651
Abstract
COVID-19 has significantly influenced living in recent years. Almost all countries have carried out all limitations to reduce its spread. Detection is highly required for further handling of COVID-19. In this study, the detection was performed using classification on 1,184 X-ray images, specifically 404 X-ray images of COVID-19 positive people, 390 X-ray images of normal people and 390 X-ray images of pneumonia positive people. The image data were extracted with the Haar wavelet algorithm and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN); each had three classification models. The Quadratic SVM model obtained the best result with an accuracy of 79.8%.
Funding(s)
Universitas Muhammadiyah Yogyakarta
Subjects
  • Classification | COVI...

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