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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF Jurnal Transformatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IKRA-ITH Informatika : Jurnal Komputer dan Informatika Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian Literasi Digital Indonesia Prosiding Seminar Riset Mahasiswa
Fahmi Amiq
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

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Implementation of DBSCAN Algorithm for Grouping Poverty Levels in Central Java Province Fahmi, Amiq; Tsani, Maulida Aristia
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8553

Abstract

Poverty is a complex problem that hampers socio-economic development in Indonesia, especially in Central Java Province, which encounters significant challenges, with a poverty rate reaching 10.77% in 2023. This study aims to identify spatial patterns of poverty in 35 districts/cities in Central Java Province by grouping areas based on the number of poor individuals reported by the Central Java Province Statistics Agency (BPS) in 2023. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm groups districts/cities based on poverty data density with optimized parameters to produce statistically significant clusters. The results of the analysis reveal four clusters, specifically cluster 0 (moderate poverty), cluster 1 (high poverty), cluster 2 (very high poverty), and cluster 3 (low poverty). Model validation was executed using the Silhouette Score (0.447) and Davies-Bouldin Index (0.441), which showed the validity of the clustering. This study is anticipated to provide strategic implications for the Central Java Provincial Government in formulating more effective poverty alleviation policies, such as resource allocation adjusted to each cluster's characteristics. In addition, this study enables future exploration of additional socio-economic factors influencing poverty, such as the Human Development Index, education, health, infrastructure, resource accessibility, and comparative analysis of clustering algorithms for enhanced accuracy.
Co-Authors Abdul Rohim Abu Salam Agus Winarno Agus Winarno, Agus Alzami, Farrikh Ardianda Aryo Prakoso Ariel Bagus Nugroho Asih Rohmani, Asih Budi Harjo Budiono Budiono Candra Irawan Catur Supriyanto Diana Purwitasari Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Fhaldian, Wahyu Fikri Budiman Hadi, Heru Pramono Harun Al Azies Hindarto, Aris Nur Husna, Farida Amila Indra Gamayanto ISWAHYUDI ISWAHYUDI Karis Widyatmoko Kurnia Desita, Raafi Lalang Erawan Lintang Mekar Tanjung Maurensa, Giacinta Mauridhi Hery Purnomo Megantara, Rama Aria Moch. Eko Rustiyono Muhammad Naufal, Muhammad Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Nova Rijati Novi Hendriyanto, Novi Pujiono Pujiono Pujiono Pujiono Pujiono Ramadhan Rakhmat Sani Ridha Rahmawati Ridho Pambudi Rizky Adrianto Sasono Wibowo Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sri Winarno Suhariyanto - Suharnawi Suharnawi Suryo Adi Nugroho Tacharri, Chusnuut Tsani, Maulida Aristia Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana