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

dc.contributor.author	Jusman, Yessi
dc.contributor.author	Mubarok, Dimas Wildan
dc.contributor.author	Riyadi, Slamet
dc.contributor.author	Kanafiah, Siti Nurul Aqmariah Mohd
dc.date.accessioned	2023-04-01T03:02:17Z
dc.date.available	2023-04-01T03:02:17Z
dc.date.issued	2022
dc.identifier.uri	http://repository.umy.ac.id/handle/123456789/36577
dc.description.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%.	en_US
dc.language.iso	en	en_US
dc.publisher	ICITACEE	en_US
dc.subject	COVID-19	en_US
dc.subject	X-ray Images	en_US
dc.subject	Haar Wavelet	en_US
dc.subject	Classification	en_US
dc.title	COVID-19 X-Ray Images Classification using Support Vector Machine and K-Nearest Neighbor	en_US
dc.type	Article	en_US

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