Spatial Clustering of Urban Villages on Stunting Babies Data in Samarinda Using the DBSCAN Model

Rizki, Nanda Arista; Asyril, Asyril; Hasan, Isran K.; Maulidah, Maulidah; Dewi, Carolina Fadia; Syahlafandi, Dhira

doi:10.30597/mkmi.v21i1.41250

Open access Jurnal

Spatial Clustering of Urban Villages on Stunting Babies Data in Samarinda Using the DBSCAN Model

Media Kesehatan Masyarakat Indonesia · Vol. 21 No. 1 (2025) · pp. 24–32

DOI: 10.30597/mkmi.v21i1.41250
ISSN: 2356-4067, 0216-2482
Published: 2025-03-31
Publisher: Faculty of Public Health, Hasanuddin University, Makassar
Language: en
License: creativecommons.org/licenses/by-nc-sa/4.0/

Abstract

The government has set an annual target to reduce stunting rates. To achieve this, the Health Department must implement well-targeted policies based on a prioritized approach, ensuring that interventions are comprehensive and coordinated for maximum effectiveness. This study aimed to cluster urban villages in Samarinda based on stunting data, including the number of cases, baby weight, and height, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model. The optimal model was selected by determining the highest silhouette score from various combinations of epsilon (ε) and MinPts values. The best results were obtained with ε = 0.95 and MinPts = 3, which produced a silhouette score of 0.432. The clustering process resulted in the formation of two primary groups, whereas four villages remained unclustered, exhibiting significant variations in the number of stunted babies. Additionally, spatial analysis revealed that stunting and malnutrition were more prevalent in densely populated urban areas, emphasizing health risks associated with population density. These findings not only provide a clearer understanding of the spatial distribution of stunting in Samarinda but also highlight the need for targeted, area-specific interventions. The insights gained from this study offer a valuable basis for prioritizing public health initiatives and developing data-driven policies to effectively address stunting in Samarinda.

Keywords

Collaboration network of the authors

12 authors · 27 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

Mulawarman University
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Muhammad Khalvin Fernanda	Mulawarman University	1
Ikmawati Ikmawati	Mulawarman University	1
Ariantje Dimpudus	Mulawarman University	1
Sumarno Ismail	Affiliation unknown	1
Tesya Sigar	Affiliation unknown	1
Salmun K. Nasib	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/mkmi/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.

Original metadata (oai_dc)

<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Spatial Clustering of Urban Villages on Stunting Babies Data in Samarinda Using the DBSCAN Model</dc:title>
	<dc:creator xml:lang="en">Rizki, Nanda Arista</dc:creator>
	<dc:creator xml:lang="en">Asyril, Asyril</dc:creator>
	<dc:creator xml:lang="en">Hasan, Isran K.</dc:creator>
	<dc:creator xml:lang="en">Maulidah, Maulidah</dc:creator>
	<dc:creator xml:lang="en">Dewi, Carolina Fadia</dc:creator>
	<dc:creator xml:lang="en">Syahlafandi, Dhira</dc:creator>
	<dc:subject xml:lang="en">Stunting</dc:subject>
	<dc:subject xml:lang="en">Urban Village</dc:subject>
	<dc:subject xml:lang="en">Spatial Clustering</dc:subject>
	<dc:subject xml:lang="en">DBSCAN</dc:subject>
	<dc:subject xml:lang="en">Silhouette Score</dc:subject>
	<dc:description xml:lang="en">The government has set an annual target to reduce stunting rates. To achieve this, the Health Department must implement well-targeted policies based on a prioritized approach, ensuring that interventions are comprehensive and coordinated for maximum effectiveness. This study aimed to cluster urban villages in Samarinda based on stunting data, including the number of cases, baby weight, and height, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model. The optimal model was selected by determining the highest silhouette score from various combinations of epsilon (ε) and MinPts values. The best results were obtained with ε = 0.95 and MinPts = 3, which produced a silhouette score of 0.432. The clustering process resulted in the formation of two primary groups, whereas four villages remained unclustered, exhibiting significant variations in the number of stunted babies. Additionally, spatial analysis revealed that stunting and malnutrition were more prevalent in densely populated urban areas, emphasizing health risks associated with population density. These findings not only provide a clearer understanding of the spatial distribution of stunting in Samarinda but also highlight the need for targeted, area-specific interventions. The insights gained from this study offer a valuable basis for prioritizing public health initiatives and developing data-driven policies to effectively address stunting in Samarinda.</dc:description>
	<dc:publisher xml:lang="en">Faculty of Public Health, Hasanuddin University, Makassar</dc:publisher>
	<dc:date>2025-03-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/mkmi/article/view/41250</dc:identifier>
	<dc:identifier>10.30597/mkmi.v21i1.41250</dc:identifier>
	<dc:source xml:lang="en">Media Kesehatan Masyarakat Indonesia; Vol. 21 No. 1: MARCH 2025; 24-32</dc:source>
	<dc:source>2356-4067</dc:source>
	<dc:source>0216-2482</dc:source>
	<dc:source>10.30597/mkmi.v21i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/mkmi/article/view/41250/13189</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 by author</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
</oai_dc:dc>