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All Journal Jurnal Teknologi Informasi dan Multimedia Research in the Mathematical and Natural Sciences
Nisky Imansyah Yahya
Program Studi Matematika, Universitas Negeri Gorontalo, Indonesia
Computer Science & IT Education Mathematics

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Analisis Komparatif Firefly Algorithm dan Particle Swarm Optimization dalam Optimasi K-Means untuk Pengelompokan Ketimpangan Pendapatan Antarprovinsi di Indonesia Adinda Adinda; Fahrezal Zubedi; Nisky Imansyah Yahya
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1026

Abstract

Income inequality among provinces in Indonesia reflects differences in welfare levels influenced by the social and economic characteristics of each region. This study aims to compare the performance of Par-ticle Swarm Optimization (PSO) and Firefly Algorithm (FA) in optimizing the number of clusters in K-Means clustering to group Indonesian provinces based on seven factors related to income inequality, namely Human Development Index (HDI), Number of Poor Population, Open Unemployment Rate, In-flation, Provincial Minimum Wage, GDP per Capita, and Labor Force Participation Rate. The data used are secondary data from 2024 covering 38 provinces. The analytical methods include data standardi-zation using Z-Score, K-Means clustering, and cluster number optimization using PSO and FA evalu-ated by Silhouette Coefficient (SC). The results show that both optimization methods improved clus-tering quality compared to K-Means without optimization, which yielded an SC of 0.23. PSO produced an SC of 0.28 with an optimal cluster number of 5, while FA produced an SC of 0.39 with an optimal cluster number of 3. FA proved superior in generating a more optimal and representative clustering structure. The clustering results reveal distinct characteristics among clusters that can serve as a ba-sis for formulating more targeted income inequality reduction policies in accordance with the charac-teristics of each regional group.
Pengelompokan Data Stunting di Indonesia Menggunakan Metode X-Means dan Agglomerative Hierarchical Clustering Nur Dhea Wahab; Salmun K. Nasib; Nurwan; Djihad Wungguli; Nisky Imansyah Yahya
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.201

Abstract

Stunting is one of the serious problems that threaten the quality of human resources in Indonesia. This study aims to analyze the patterns and characteristics of stunting in Indonesia by applying the X-Means clustering method and Agglomerative Hierarchical Clustering (AHC). The X-Means method is used to determine the optimal number of clusters automatically by utilizing the Bayesian Information Criterion (BIC), while AHC forms a dendrogram to understand the multilevel structure of the clusters formed. Based on the analysis, the X-Means method produces three optimal clusters with the smallest BIC value of 651.9475, where cluster 1 consists of 17 provinces, cluster 2 includes 12 provinces, and cluster 3 includes 5 provinces. The AHC method with the Single Linkage approach also produced three optimal clusters, with cluster 1 covering 32 provinces, cluster 2 consisting of 1 province (West Nusa Tenggara), and cluster 3 covering 1 province (East Nusa Tenggara), as well as the highest Silhouette Index value of 0.28. The results show that both methods provide a comprehensive picture of stunting patterns in Indonesia, which can be used as a basis for designing more targeted intervention programs according to the characteristics of each cluster. This data-driven strategy is expected to increase policy effectiveness in reducing stunting in Indonesia.
Co-Authors Adinda Adinda Djihad Wungguli FAHREZAL ZUBEDI Nur Dhea Wahab Nurwan Salmun K. Nasib