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Open access Jurnal SINTA 2

SMOTE-SVM for Handling Imbalanced Data in Obesity Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 141–152

DOI
10.22146/ijccs.103994
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.

Keywords

Collaboration network of the authors

17 authors · 33 co-authorship links

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

  • Universitas Ahmad Dahlan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abdul FadlilUniversitas Ahmad Dahlan2
Bella Okta Sari MirandaUniversitas Ahmad Dahlan2
Ainunna’imahUniversitas Ahmad Dahlan1
Imam RiadiUniversitas Ahmad Dahlan1
Muhajir YunusUniversitas Ahmad Dahlan1
Maulana Muhammad Jogo SamodroUniversitas Ahmad Dahlan1
Herman YuliansyahUniversitas Ahmad Dahlan1
Kintung PrayitnoAffiliation unknown1
SunardiAffiliation unknown1
Kintung PrayitnoUniversitas Ahmad Dahlan1
Herman HermanAffiliation unknown1
Aldibangun Pidekso PutroAffiliation unknown1
Herman HermanAffiliation unknown1

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">SMOTE-SVM for Handling Imbalanced Data in Obesity Classification</dc:title>
	<dc:creator>Biddinika, Muhammad Kunta</dc:creator>
	<dc:creator>Yuliansyah, Herman</dc:creator>
	<dc:creator>Soyusiawaty, Dewi</dc:creator>
	<dc:creator>Razak, Farhan Radhiansyah</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Obesity; SMOTE; RUS; RBF; Linear</dc:subject>
	<dc:description xml:lang="en-US"> Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.</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>2025-04-30</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/103994</dc:identifier>
	<dc:identifier>10.22146/ijccs.103994</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 141-152</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/103994/41522</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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