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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) · hlm. 177–196

DOI
10.5614/jpwk.2022.33.2.3
ISSN
2502-6429
Terbit
2022-08-27
Penerbit
The Directorate for Research and Innovation, Institut Teknologi Bandung
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0/

Abstrak

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.

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  • 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
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Rekan penulisOrganisasiArtikel bersama
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 KurniawanAfiliasi tidak diketahui1
Muhammad ZulfadhliInstitut Teknologi Sepuluh Nopember1
Riwi Dyah PangestiUniversity of Bengkulu1
Albertus Eka Putra HaryantoAfiliasi tidak diketahui1
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 HanikeAfiliasi tidak diketahui1
Achmad ChoiruddinInstitut Teknologi Sepuluh Nopember1
Raupong RaupongHasanuddin University1
Muhammad AkbarAfiliasi tidak diketahui1
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

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Sumber metadata: OAI-PMH (metadata jurnal) http://journals.itb.ac.id/index.php/jpwk/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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>