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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Aina Musdholifah
Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
Computer Science & IT Control & Systems Engineering

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GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification Dias Aziz Pramudita; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41215

Abstract

Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM  to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kernel
Co-Authors Dias Aziz Pramudita