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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Buana Informatika Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Prosiding SNATIF JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIV : International Journal on Informatics Visualization Jurnal Ilmiah FIFO JRST (Jurnal Riset Sains dan Teknologi) Jurnal Sisfokom (Sistem Informasi dan Komputer) IJISTECH (International Journal Of Information System & Technology) JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Inti Talafa : Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Abdi Masyarakat Indonesia Techno Urecol Journal. Part E: Engineering Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics ABDIMASTEK
Lisna Zahrotun
Universitas Ahmad Dahlan
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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K-MEANS WITH PARTICLE SWARM OPTIMIZATION FOR ERROR REDUCTION IN MICRO, SMALL, MEDIUM ENTERPISE CRAFT IN YOGYAKARTA Athallah Naufal Muthahhari; Lisna Zahrotun
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40664

Abstract

Purpose: Micro, Small, and Medium Enterprises (MSMEs) in the handicraft sector of Yogyakarta face significant challenges regarding capital access and marketing optimization. This study aims to group 146 MSMEs based on business characteristics to support the formulation of targeted empowerment strategies. Methods/Study design/approach: A quantitative approach was employed using survey data processed through encoding and normalization. The research utilized the K-Means algorithm, optimized with Particle Swarm Optimization (PSO) to determine the optimal number of clusters, and evaluated the model using Sum of Squared Error (SSE) and Mean Absolute Error (MAE). Result/Findings: The results show that the K-Means method optimized with PSO significantly outperforms the standard K-Means algorithm. Specifically, the optimized model achieved an SSE of 51.676 and an MAE of 0.116, compared to the standard K-Means algorithm which produced a higher SSE of 54.555 and an MAE of 0.124. Novelty/Originality/Value: The novelty of this study lies in the application of PSO to minimize clustering errors specifically within the Yogyakarta handicraft sector context. These findings offer a highly accurate, data-driven foundation for policymakers to design effective MSME development programs. 
K-Means Centroid Optimization with Genetic Algorithm for Clustering Micro, Small, Medium Enterprises in Yogyakarta Muhammad Faris Akbar; Lisna Zahrotun
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25480

Abstract

K-Means is a widely used data clustering algorithm due to its simplicity and fast performance. However, the weakness of K-Means is in determining the cluster centroid randomly, which can result in suboptimal clustering results, especially since it tends to get stuck on local solutions. This research aims to overcome this weakness by integrating the Genetic Algorithms (GA) into the K-Means process, optimizing the initial centroid, and improving clustering quality. The method combines GA with K-Means on MSME data in Yogyakarta, where GA rearranges the cluster's initial centroid more optimally. The results showed that this method reduced the average value of the Davies-Bouldin Index (DBI) from 1,819 in conventional K-Means to 1,349 with GA integration, indicating an improvement in cluster quality by 25.9%. These results prove that integration of GA with K-Means improves clustering accuracy and improves cluster separation, as measured by a significant decrease in DBI value
Co-Authors Amanatullah, Yosyadi Rizkika Anita Rahmawati Anna Hendri Soleliza Jones Ansari Ansari Arfiani Nur Khusna Aryandita, Muhammad Dwinda Agust Athallah Naufal Muthahhari Dewi Soyusiawaty Dwiyanti, Ryzky Aulia Elyana, Ade Eni Purwaningsih Fajri, Yunus Fasihullisan Damopolii, Muhamad Rizki Fiqriany Karepesina Hanifatun, Farras Hapsari, Intan Herri Kurnia Ika Arfiani Ika Kurnia Saputri Indri Dwi Lestari Jones, Anna Hendri Soleliza Juniarti, Rahmat Khoirunnisa, Ninda Kurnia, Herri Lintang Fauziyatu Azmi Muhammad Faishal Muhammad Faris Akbar Muhammad Fharahbi Fachri Mushlihudin Mushlihudin Mushlihudin Mushlihudin Mushlihudin, Mushlihudin Mushlihudin, Mushlihudin Purwayoga, Vega Ramadhan, Imam Rusydi Umar Sabila, Liya Yusrina Sari, Hana Jelita Siti Jamilatun Soleliza Jones, Anna Hendri Soleliza Jones, Anna Hendry Suandi As, Banu Harli Trimulya Taufik Mufti Haryadi Tedy Setiadi Tsani Elvia Nita Utaminingsih Linarti, Utaminingsih