IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Vol 14, No 1 (2020): January

GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

Dias Aziz Pramudita (Program Studi S2 Ilmu Komputer FMIPA UGM, Yogyakarta)
Aina Musdholifah (Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia)



Article Info

Publish Date
31 Jan 2020

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

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Journal Info

Abbrev

ijccs

Publisher

Subject

Computer Science & IT Control & Systems Engineering

Description

Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so ...