Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC)

Ni'matuzzahroh, Ludia; Dani, Andrea Tri Rian; Adrianingsih, Narita Yuri

doi:10.20956/j.v19i1.20882

Open access Jurnal

Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC)

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 1 (2022) · pp. 79–89

DOI: 10.20956/j.v19i1.20882
ISSN: 2614-8811, 1858-1382
Published: 2022-09-07
Publisher: Department of Mathematics, Hasanuddin University
Language: en
License: creativecommons.org/licenses/by/4.0

Abstract

Cluster analysis is a statistical analysis that can group objects of observation into several groups/clusters based on their similarity of characteristics. The grouping into several clusters is based on the information contained in the object under study. A cluster can be said to be good if it has high internal homogeneity and high external heterogeneity. The clustering method used in this study is the agglomerate hierarchical clustering (AHC) method, where the cluster formation algorithm used in this AHC method is average linkage, single linkage, complete linkage, and ward. Cluster analysis using the AHC method will be applied to poverty indicator data for Regencies/Cities in Kalimantan Island, which consists of several variables. This study aims to obtain the optimal results of grouping Regencies/Cities in Kalimantan Island, with the number of clusters that have been determined at the beginning, namely as many as 3 clusters. Based on the results of the analysis using the AHC method, the ward algorithm produces an agglomerate coefficient value of 0.89, where this value is close to 1, which means that the ward algorithm is the best in clustering Regencies/Cities in Kalimantan Island.

Keywords

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Co-author	Organisation	Joint articles
Sifriyani Sifriyani	Affiliation unknown	4
Anggi Putri Anisar	Affiliation unknown	1
Zakiyah Mar'ah	Affiliation unknown	1
Meirinda Fauziyah	Affiliation unknown	1
Rahmania Rahmania	Affiliation unknown	1
Rizki Dwi Fadlirhohim	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/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC) </dc:title>
	<dc:creator xml:lang="en">Ni'matuzzahroh, Ludia</dc:creator>
	<dc:creator xml:lang="en">Dani, Andrea Tri Rian </dc:creator>
	<dc:creator xml:lang="en">Adrianingsih, Narita Yuri </dc:creator>
	<dc:subject xml:lang="en">Agglomerative Hierarchical Clustering</dc:subject>
	<dc:subject xml:lang="en">Cluster</dc:subject>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:description xml:lang="en">Cluster analysis is a statistical analysis that can group objects of observation into several groups/clusters based on their similarity of characteristics. The grouping into several clusters is based on the information contained in the object under study. A cluster can be said to be good if it has high internal homogeneity and high external heterogeneity. The clustering method used in this study is the agglomerate hierarchical clustering (AHC) method, where the cluster formation algorithm used in this AHC method is average linkage, single linkage, complete linkage, and ward. Cluster analysis using the AHC method will be applied to poverty indicator data for Regencies/Cities in Kalimantan Island, which consists of several variables. This study aims to obtain the optimal results of grouping Regencies/Cities in Kalimantan Island, with the number of clusters that have been determined at the beginning, namely as many as 3 clusters. Based on the results of the analysis using the AHC method, the ward algorithm produces an agglomerate coefficient value of 0.89, where this value is close to 1, which means that the ward algorithm is the best in clustering Regencies/Cities in Kalimantan Island.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-09-07</dc:date>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/20882</dc:identifier>
	<dc:identifier>10.20956/j.v19i1.20882</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 1 (2022): SEPTEMBER, 2022; 79-89</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/20882/8611</dc:relation>
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	<dc:rights xml:lang="en">Copyright (c) 2022 Author and publisher</dc:rights>
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