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

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      Date
      2022
      Author
      Jusman, Yessi
      Mubarok, Dimas Wildan
      Riyadi, Slamet
      Kanafiah, Siti Nurul Aqmariah Mohd
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      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%.
      URI
      http://repository.umy.ac.id/handle/123456789/36577
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