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Open access Jurnal SINTA 3

MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1653–1662

DOI
10.30598/barekengvol17iss3pp1653-1662
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users, the number of small and micro industries and the number of tourist villages n cluster 2, the amount of village tours. In cluster 3, the percentage of users of electricity and the percentage of villages that have mining and quarrying.

Collaboration network of the authors

40 authors · 111 co-authorship links · 2 across different organisations · showing the 40 most connected

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

  • IPB University
  • Bandung Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Khairil Anwar NotodiputroBandung Islamic University11
Anwar FitriantoAffiliation unknown3
Sigit NugrohoAffiliation unknown3
Elisabeth Evelin KarunaAffiliation unknown2
Anang KurniaAffiliation unknown2
Idhia SrilianaAffiliation unknown2
Agus Mohamad SolehIPB University2
Nurul HidayatiAffiliation unknown2
Muh. SunanIPB University1
Unique Desyrre A. ResiloyIPB University1
Desy EndrianiIPB University1
Cici SuhaeniIPB University1
Gerry Alfa DitoIPB University1
Leni Anggraini SusantiIPB University1
Dwi Agustin Nuriani SirodjAffiliation unknown1
Novi PuspitaAffiliation unknown1
Farit Mochamad AfendiAffiliation unknown1
Vera Maya SantiAffiliation unknown1
Indahwati IndahwatiAffiliation unknown1
kusnaeni kusnaeniAffiliation unknown1
Agus M SolehAffiliation unknown1
Farit M AfendiAffiliation unknown1
Hafizah IlmaAffiliation unknown1
Ni Gusti Ayu Putu Puteri SuantariAffiliation unknown1
Asrirawan AsrirawanAffiliation unknown1
Tommy JomechoAffiliation unknown1
Cinta Rizky OktarinaAffiliation unknown1
Stevy Cahya PratiwiAffiliation unknown1
Muhammad Azis SuprayogiAffiliation unknown1
I Made SumertajayaAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Arie PurwantoAffiliation unknown1
Esther Damayanti SihombingAffiliation unknown1
Pepi NoviantiAffiliation unknown1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA</dc:title>
	<dc:creator>Meylisah, Eni</dc:creator>
	<dc:creator>Rini, Dyah Setyo</dc:creator>
	<dc:creator>Fransiska, Herlin</dc:creator>
	<dc:creator>Agwil, Winalia</dc:creator>
	<dc:creator>Sartono, Bagus</dc:creator>
	<dc:description xml:lang="en-US">When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users  , the number of small and micro industries  and the number of tourist villages  n cluster 2, the amount of village tours  . In cluster 3, the percentage of users of electricity  and the percentage of villages that have mining and quarrying  .</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8785</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1653-1662</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1653-1662</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8785/6579</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Eni Meylisah, Dyah Setyo Rini, Herlin Fransiska, Winalia Agwil, Bagus Sartono</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>