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A COMPARATIVE ANALYSIS OF DBSCAN AND GAUSSIAN MIXTURE MODEL FOR CLUSTERING INDONESIAN PROVINCES BASED ON SOCIOECONOMIC WELFARE INDICATORS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 3 (2025) · hlm. 2039–2056

DOI
10.30598/barekengvol19iss3pp2039-2056
ISSN
2615-3017, 1978-7227
Terbit
2025-07-01
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Public welfare refers to a condition in which people experience happiness, comfort, prosperity, and can adequately fulfill their basic needs. Indonesia consists of several provinces, each with varying levels of welfare. One crucial aspect in promoting equitable development is ensuring that all regions in Indonesia achieve similar welfare standards. This study aims to classify Indonesian provinces based on socioeconomic welfare indicators, with the results serving as a basis for policy-making that considers regional potential and challenges. The data used in this study are secondary data obtained from the official website of BPS-Statistics Indonesia on provincial welfare indicators from 2020 to 2023. The research methodology includes data collection, descriptive statistical analysis, determining the optimal number of clusters, and comparing the clustering performance of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Gaussian Mixture Model (GMM) using Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index as evaluation metrics. The DBSCAN-based clustering resulted in two clusters: high-welfare and low-welfare regions. Meanwhile, GMM clustering produced five clusters: moderate, fairly low, low, high, and fairly high welfare regions. Based on cluster validity measures, GMM outperformed DBSCAN, achieving a Silhouette score of 0.28, a Davies-Bouldin Index of 1.12, and a Calinski-Harabasz Index of 10.9.

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  • Yogyakarta State University
  • Universitas Gadjah Mada
  • State University of Malang
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Universitas Nahdlatul Ulama Purwokerto
  • State University of Makassar
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Rekan penulisOrganisasiArtikel bersama
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Djemari MardapiYogyakarta State University1
Lusi HariniYogyakarta State University1
Hartono HartonoYogyakarta State University1
Afifah HayatiUniversitas Nahdlatul Ulama Purwokerto1
Syamsudin Mas'udState University of Makassar1
K KaryatiYogyakarta State University1
Fitriana Yuli SaptaningtyasYogyakarta State University1
Mei Shinta SetiawanState University of Malang1
Nur EvaState University of Malang1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A COMPARATIVE ANALYSIS OF DBSCAN AND GAUSSIAN MIXTURE MODEL FOR CLUSTERING INDONESIAN PROVINCES BASED ON SOCIOECONOMIC WELFARE INDICATORS</dc:title>
	<dc:creator>Andayani, Sri</dc:creator>
	<dc:creator>Retnani, Namita</dc:creator>
	<dc:creator>Yusri, Thesa Adi Saputra</dc:creator>
	<dc:creator>Marwoto, Bambang Sumarno Hadi</dc:creator>
	<dc:description xml:lang="en-US">Public welfare refers to a condition in which people experience happiness, comfort, prosperity, and can adequately fulfill their basic needs. Indonesia consists of several provinces, each with varying levels of welfare. One crucial aspect in promoting equitable development is ensuring that all regions in Indonesia achieve similar welfare standards. This study aims to classify Indonesian provinces based on socioeconomic welfare indicators, with the results serving as a basis for policy-making that considers regional potential and challenges. The data used in this study are secondary data obtained from the official website of BPS-Statistics Indonesia on provincial welfare indicators from 2020 to 2023. The research methodology includes data collection, descriptive statistical analysis, determining the optimal number of clusters, and comparing the clustering performance of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Gaussian Mixture Model (GMM) using Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index as evaluation metrics. The DBSCAN-based clustering resulted in two clusters: high-welfare and low-welfare regions. Meanwhile, GMM clustering produced five clusters: moderate, fairly low, low, high, and fairly high welfare regions. Based on cluster validity measures, GMM outperformed DBSCAN, achieving a Silhouette score of 0.28, a Davies-Bouldin Index of 1.12, and a Calinski-Harabasz Index of 10.9.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17533</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss3pp2039-2056</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application; 2039-2056</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss3year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17533/10825</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Sri Andayani, Namita Retnani, Thesa Adi Saputra Yusri, Bambang Sumarno Hadi Marwoto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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