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Jurnal SINTA 2

Hybrid Genetic Algorithm and Particle Swarm Optimization as Centroid Initialization in K-Means Clustering for National Nutrition Dataset

Journal of ICT Research and Applications (JICTRA) · Vol. 20 No. 2 (2026) · pp. 183–204

DOI
10.5614/itbj.ict.res.appl.2026.20.2.5
ISSN
2338-5499, 2337-5787
Published
2026-08-27
Publisher
DRI - ITB
Language
eng

Abstract

The efficacy of large-scale public health interventions, such as Indonesia’s Makan Bergizi Gratis (MBG) program, relies heavily on the precise identification of vulnerable regions. Conventional clustering techniques like K-means are essential for such mapping but frequently suffer from performance degradation due to sensitivity to random centroid initialization, leading to suboptimal and unstable solutions. This study proposes a sequential hybrid metaheuristic framework (K-means+GAPSO) that synergizes the broad global exploration of genetic algorithms (GA) with the rapid local exploitation of particle swarm optimization (PSO). The proposed model was rigorously validated using the Indonesian National Nutrition Dataset (2021–2023) through 10 independent runs to ensure stochastic robustness. Computational results at the optimal cluster count (K = 3) demonstrated that the K-means+GAPSO (prob = 0.3) configuration significantly outperformed the standard baselines, achieving a highly stable silhouette mean of 0.7130 (±0.0036) and a Davies-Bouldin index mean of 0.4708 (±0.0178). This metric achievement represents a substantial performance improvement in separation and compactness compared to standard K-means. The implementation of this structurally robust method for nutritional clustering provides a reliable analytical foundation for policymakers to accurately target food security initiatives and minimize resource misallocation.

Keywords

Collaboration network of the authors

6 authors · 9 co-authorship links

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  • Diponegoro University
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Ahmad WijayantoDiponegoro University1
Rukun SantosoDiponegoro University1

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://journals.itb.ac.id/index.php/jictra/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">Hybrid Genetic Algorithm and Particle Swarm Optimization as Centroid Initialization in K-Means Clustering for National Nutrition Dataset</dc:title>
	<dc:creator>Somantri, Maman</dc:creator>
	<dc:creator>Pramudya, Reza Iqbal</dc:creator>
	<dc:creator>Sugiharto, Aris</dc:creator>
	<dc:creator>Kurniawan, Tonni Agustiono</dc:creator>
	<dc:subject xml:lang="en-US">genetic algorithm</dc:subject>
	<dc:subject xml:lang="en-US">hybrid clustering</dc:subject>
	<dc:subject xml:lang="en-US">K-means</dc:subject>
	<dc:subject xml:lang="en-US">national nutrition</dc:subject>
	<dc:subject xml:lang="en-US">particle swarm optimization</dc:subject>
	<dc:description xml:lang="en-US">The efficacy of large-scale public health interventions, such as Indonesia’s Makan Bergizi Gratis (MBG) program, relies heavily on the precise identification of vulnerable regions. Conventional clustering techniques like K-means are essential for such mapping but frequently suffer from performance degradation due to sensitivity to random centroid initialization, leading to suboptimal and unstable solutions. This study proposes a sequential hybrid metaheuristic framework (K-means+GAPSO) that synergizes the broad global exploration of genetic algorithms (GA) with the rapid local exploitation of particle swarm optimization (PSO). The proposed model was rigorously validated using the Indonesian National Nutrition Dataset (2021–2023) through 10 independent runs to ensure stochastic robustness. Computational results at the optimal cluster count (K = 3) demonstrated that the K-means+GAPSO (prob = 0.3) configuration significantly outperformed the standard baselines, achieving a highly stable silhouette mean of 0.7130 (±0.0036) and a Davies-Bouldin index mean of 0.4708 (±0.0178). This metric achievement represents a substantial performance improvement in separation and compactness compared to standard K-means. The implementation of this structurally robust method for nutritional clustering provides a reliable analytical foundation for policymakers to accurately target food security initiatives and minimize resource misallocation.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2026-08-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/26757</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2026.20.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 20 No. 2 (2026); 183-204</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2026.20.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/26757/7462</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Journal of ICT Research and Applications</dc:rights>
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