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All Journal Seminar Nasional Informatika (SEMNASIF) JITEK (Jurnal Ilmiah Teknosains) SMARTICS Journal INTEK: Informatika dan Teknologi Informasi Surya Abdimas Jurnal Sistem Cerdas ComTech: Computer, Mathematics and Engineering Applications Progresif: Jurnal Ilmiah Komputer Cyber Security dan Forensik Digital (CSFD) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Dialektika Informatika (Detika) Bulletin of Computer Science Research Journal Of Information System And Artificial Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Informasi Kesehatan Nasional Jurnal Lentera Pengabdian SmartComp Akademika
Ike Yunia Pasa
Universitas Muhummadiyah Purworejo
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Journal : Bulletin of Computer Science Research

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Analisis Klasterisasi Wilayah Berdasarkan Tingkat Kepadatan Penduduk Menggunakan Algoritma K-Means Berbasis Sistem Informasi Geografis Athallah, Mustafa Iffat Shafi; Saputro, Wahju Tjahjo; Pasa, Ike Yunia
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1015

Abstract

This study aims to analyze and map the population density of regencies and municipalities in Jawa Tengah using a spatial analysis approach based on Geographic Information Systems (GIS) and the K-Means clustering algorithm. The main issue addressed is the lack of systematically classified and informative population density mapping to support spatial analysis and regional decision-making. Secondary data were obtained from the Central Bureau of Statistics (BPS), including total population, population growth rate, population percentage, population density per square kilometer, and administrative boundary spatial data. Prior to clustering, all variables were normalized using the Min-Max scaling method to prevent bias caused by differences in variable ranges in Euclidean distance calculations. The research employed a quantitative descriptive method with K-Means (K=3) to classify regions into low, medium, and high population density clusters. The results indicate that out of 35 regencies/municipalities, 7 regions (20%) fall into the high-density cluster, 22 regions (62.86%) into the medium-density cluster, and 6 regions (17.14%) into the low-density cluster. The implementation of the clustering results into a thematic map using a color scheme of red (high), yellow (medium), and green (low) effectively visualizes spatial distribution patterns, thereby supporting data-driven regional planning and spatial-based policy formulation.
Co-Authors Agung Nugroho Ahamd Mubaroq, Masnu Ahmad Kurniawan Al Fatoni, Firmansyah Anam, M Khairul Anggoro, Cahyo Anisa Rahayu Arif Tri Widiyatmoko Arum Ratnaningsih Athallah, Mustafa Iffat Shafi Aviani, Anggi Cahyo Anggoro Daniel Alexander Octavianus Turang Dedy Hariyadi Delphine Yusticia Ratnasari Dhea Kalingga Lintang Dwiki Andrianto Efrizoni, Lusiana Faizatun Karomah Fakhri Khairi Febriarini, Nurul Isnaini Hastiningrum, Citra Annisa Irvan Rifa'i Jumasa, Hamid Muhammad Kartina Diah K. W. Khairi, Fakhri Krisna Widatama Lidia Putri Fitriana Lindawati, Meitri Muhammad Bambang Firdaus Murhadi Murhadi Murhadi Murhadi Murhadi Nuh Hanan, Martin Nur Wachid Adi Prasetya Nurhayati Nurhayati Nurhayati Nurul Bahiyah Nurul Isnaini Febriarini Prisnadela Purwanto, Muhammad Ardi Putu Sudira Ratih Hafsarah Maharrani Satria, Mohamad Ganda Suryadi, Agung Tjahjo Saputro, Wahju Tri Rochmadi Wahju Tjahjo Saputro Wuri Setyowardani Yoga Sahria