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Partitional Clustering of Underdeveloped Area Infrastructure with Unsupervised Learning Approach: A Case Study in the Island of Java, Indonesia

Journal of Regional and City Planning · Vol. 33 No. 2 (2022) · pp. 177–196

DOI
10.5614/jpwk.2022.33.2.3
ISSN
2502-6429
Published
2022-08-27
Publisher
The Directorate for Research and Innovation, Institut Teknologi Bandung
Language
eng
License
creativecommons.org/licenses/by-nc/4.0/

Abstract

This study attempted to identify underdeveloped areas in regencies/cities on the island of Java, Indonesia, based on a number of infrastructure indicators. An unsupervised learning approach was used to perform partition clustering with the K-Means, K-Medoids, and CLARA methods. In addition to technically obtaining clustering results and conducting a performance comparison of the three unsupervised learning methods, another objective of this research was to map the clustering results to make it easier to recognize the characteristics of the regions indicated as underdeveloped areas, which should be absolute priorities for infrastructure development. It was found that the best clustering method was the CLARA method, with a connectivity coefficient of 7.4794 and a Dunn’s index value of 0.1042. The partition clustering of regencies/cities on Java Island using the CLARA method based on infrastructure indicators resulted in 99 regencies/cities included in the cluster of areas with underdeveloped infrastructure, while 12 regencies/cities were included in the cluster of areas with developing infrastructure, and 8 regencies/cities were included in the cluster of areas with developed infrastructure.

Keywords

Collaboration network of the authors

31 authors · 77 co-authorship links · 34 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • Universitas Dian Nuswantoro
  • Universitas Islam Negeri Maulana Malik Ibrahim
  • Universitas Muhammadiyah Pekajangan Pekalongan
  • University of Bengkulu
  • Sekolah Tinggi Agama Islam Negeri Majene
  • Politeknik STIA LAN Bandung
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Angga Dwi MulyantoInstitut Teknologi Sepuluh Nopember2
Sri HariniUniversitas Islam Negeri Maulana Malik Ibrahim1
Jerry Dwi Trijoyo PurnomoInstitut Teknologi Sepuluh Nopember1
Fachrur RoziUniversitas Islam Negeri Maulana Malik Ibrahim1
Hairur RahmanUniversitas Islam Negeri Maulana Malik Ibrahim1
Iwan KurniawanPoliteknik STIA LAN Bandung1
Muhammad Idham KurniawanAffiliation unknown1
Muhammad ZulfadhliInstitut Teknologi Sepuluh Nopember1
Riwi Dyah PangestiUniversity of Bengkulu1
Albertus Eka Putra HaryantoAffiliation unknown1
D DarwisSekolah Tinggi Agama Islam Negeri Majene1
Cindy Cahyaning AstutiUniversitas Muhammadiyah Sidoarjo1
Abdul KhairUniversitas Islam Negeri Palangka Raya1
Noraida NoraidaPoltekkes Kemenkes Banjarmasin1
Santi Wulan PurnamiInstitut Teknologi Sepuluh Nopember1
Abdul Malik BalamiInstitut Teknologi Sepuluh Nopember1
Yusrianti HanikeAffiliation unknown1
Achmad ChoiruddinInstitut Teknologi Sepuluh Nopember1
Raupong RaupongHasanuddin University1
Muhammad AkbarAffiliation unknown1
Muljono MuljonoUniversitas Dian Nuswantoro1
Pulung Nurtantio AndonoUniversitas Dian Nuswantoro1
Muhammad NaufalUniversitas Dian Nuswantoro1
Sari Ayu WulandariUniversitas Dian Nuswantoro1
Sugiharto ShUniversitas Muhammadiyah Pekajangan Pekalongan1
Wiwiek NatalyaUniversitas Muhammadiyah Pekajangan Pekalongan1

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) http://journals.itb.ac.id/index.php/jpwk/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Partitional Clustering of Underdeveloped Area Infrastructure with Unsupervised Learning Approach: A Case Study in the Island of Java, Indonesia</dc:title>
	<dc:creator>Otok, Bambang Widjanarko</dc:creator>
	<dc:creator>Suharsono, Agus</dc:creator>
	<dc:creator>Purhadi, Purhadi</dc:creator>
	<dc:creator>Standsyah, Rahmawati Erma </dc:creator>
	<dc:creator>Al Azies, Harun</dc:creator>
	<dc:subject xml:lang="en-US">CLARA clustering</dc:subject>
	<dc:subject xml:lang="en-US">infrastructure</dc:subject>
	<dc:subject xml:lang="en-US">underdeveloped areas</dc:subject>
	<dc:subject xml:lang="en-US">unsupervised learning</dc:subject>
	<dc:description xml:lang="en-US">This study attempted to identify underdeveloped areas in regencies/cities on the island of Java, Indonesia, based on a number of infrastructure indicators. An unsupervised learning approach was used to perform partition clustering with the K-Means, K-Medoids, and CLARA methods. In addition to technically obtaining clustering results and conducting a performance comparison of the three unsupervised learning methods, another objective of this research was to map the clustering results to make it easier to recognize the characteristics of the regions indicated as underdeveloped areas, which should be absolute priorities for infrastructure development. It was found that the best clustering method was the CLARA method, with a connectivity coefficient of 7.4794 and a Dunn’s index value of 0.1042. The partition clustering of regencies/cities on Java Island using the CLARA method based on infrastructure indicators resulted in 99 regencies/cities included in the cluster of areas with underdeveloped infrastructure, while 12 regencies/cities were included in the cluster of areas with developing infrastructure, and 8 regencies/cities were included in the cluster of areas with developed infrastructure.</dc:description>
	<dc:publisher xml:lang="en-US">The Directorate for Research and Innovation, Institut Teknologi Bandung</dc:publisher>
	<dc:date>2022-08-27</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-US">Research Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jpwk/article/view/17051</dc:identifier>
	<dc:identifier>10.5614/jpwk.2022.33.2.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Regional and City Planning; Vol. 33 No. 2 (2022); 177-196</dc:source>
	<dc:source>2502-6429</dc:source>
	<dc:source>10.5614/jpwk.2022.33.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jpwk/article/view/17051/5917</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of Regional and City Planning</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
</oai_dc:dc>