Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification

Authors

  • Sri Suryani Prasetiyowati
  • Sri Harini
  • Juniardi Nur Fadila
  • Hilda Fahlena

DOI:

https://doi.org/10.21108/ijoict.v10i1.965

Keywords:

Support Vector Machine, Kernel, Parameter, Signifikansi, Covid-19

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. 

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Published

2024-07-25

How to Cite

Prasetiyowati, S. S., Harini, S., Nur Fadila, J., & Fahlena, H. . (2024). Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification. International Journal on Information and Communication Technology (IJoICT), 10(1), 127–140. https://doi.org/10.21108/ijoict.v10i1.965

Issue

Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024

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Articles

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