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GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 1 (2020) · pp. 11–22

DOI
10.22146/ijccs.41215
ISSN
2460-7258, 1978-1520
Published
2020-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Keywords

Collaboration network of the authors

28 authors · 46 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Politeknik Negeri Ketapang
  • National Central University
  • Muria Kudus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada4
Anny Kartika SariUniversitas Gadjah Mada3
Sri T. HartatiUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Fajar Ibnu TufeilAffiliation unknown1
Mujiono MujionoAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Ria AstriratmaUniversitas Gadjah Mada1
Agus HarjokoUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Damar RiyadiUniversitas Gadjah Mada1
Ilham UnggaraUniversitas Gadjah Mada1
Yessi YunitasariUniversitas Gadjah Mada1
Abidah ElcholiqiUniversitas Gadjah Mada1
Wahyu SrimulyaniUniversitas Gadjah Mada1
Hans Satria KusumaUniversitas Gadjah Mada1
Yunita SariUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Faisal RamadhanUniversitas Gadjah Mada1
Muhammad Bintang BahyUniversitas Gadjah Mada1
Muhammad Fawwaz MaydaUniversitas Gadjah Mada1
Aditya PermanaUniversitas Gadjah Mada1
Timothy K. ShihNational Central University1
Anastasya LatubessyMuria Kudus University1
Sri KusrohmaniahUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification</dc:title>
	<dc:creator>Pramudita, Dias Aziz</dc:creator>
	<dc:creator>Musdholifah, Aina</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Gravitational Search Algorithm; SVM; Thyroid Nodule; GSA-SVM</dc:subject>
	<dc:description xml:lang="en-US">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</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/41215</dc:identifier>
	<dc:identifier>10.22146/ijccs.41215</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 1 (2020): January; 11-22</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/41215/26953</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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