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All Journal IJoICT (International Journal on Information and Communication Technology) Communication in Biomathematical Sciences Jurnal Matematika UNAND Madani : Indonesian Journal of Civil Society Jurnal AbdiMas Nusa Mandiri Jurnal Pengabdian Sosial
Hilda Fahlena
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification Prasetiyowati, Sri Suryani; Harini, Sri; Nur Fadila, Juniardi; Fahlena, Hilda
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.965

Abstract

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.
Co-Authors Achmad Lukman, Achmad Admi Nazra Agung Toto Wibowo Ambar Winarni Azhary Ramadhanty Dancent Sutanto Eduardus Axel Wijaya Edy Soewono Fakhruddin, Muhammad Farida Fatkhur Rohim I Wayan Palton Anuwiksa Jane T. M. Sahetapy-Engel Juni Wijayanti Puspita Juniardi Nur Fadila Muhammad Fakhruddin Nuning Nuraini Nur Fadila, Juniardi Nur Ghaniaviyanto Ramadhan Pratama, Marchelle Fernanda Putri W., Amanda Kayla Ramadhanty, Azhary Rohim, Fatkhur Sahetapy-Engel, Jane T. M. Sri Harini Sri Harini Sri Suryani Prasetiyowati Stephania, Celenie Patrisse Sudirman Sutanto, Dancent sutimin sutimin Syafwan, Mahdhivan Tarihoran, Ekmal Reyhan Tsamarrah, Fadlil Taufani Warih Maharani Widya Oktaviana Wijaya, Eduardus Axel