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Fahrezal Zubedi
Program Studi Statistika, Universitas Negeri Gorontalo, Indonesia
Computer Science & IT

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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.
Perbandingan Metode Biclustering untuk Pengelompokan Wilayah Berdasarkan Faktor Penyebab Kusta di Sulawesi Revo Asiki; Fahrezal Zubedi; Armayani Arsal
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.v8i3.1028

Abstract

Leprosy is one of the health issues that spread due to various social and environmental factors. It is a public health issue whose spread is influenced by various social and environmental factors. The aim of this study is to group districts/cities on the island of Sulawesi based on the factors that influence the spread of leprosy using a biclustering approach. The methods used were Cheng & Church (CC) and Iterative Signature Algorithm (ISA), which can cluster data simultaneously on the dimensions of area and variables. The data used are secondary data from 2024 covering eight variables and a number of districts/cities as the units of observation. The analysis stages included pre-processing, missing value handling using mean imputation, data standardisation, and application of the two biclustering methods. The performance was evaluated using Mean Squared Residue (MSR), the Liu & Wang index, and variance. The results of the study show that the CC method produces an average MSR value of 0.006015, which is lower than the ISA method's value of 0.015006. The average Liu & Wang index value for the CC method was 0.2905, lower than the ISA method's value of 0.7356. Furthermore, the average variance in the CC method was 0.07737, which was lower than the ISA method at 2.7859. Based on these three evaluation criteria, the Cheng & Church method is more effective in grouping regions based on factors influencing the spread of leprosy in Sulawesi.
Co-Authors Adinda Adinda Armayani Arsal Nisky Imansyah Yahya Revo Asiki