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A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 1 (2025) · pp. 129–140

DOI
10.30598/barekengvol19iss1pp129-140
ISSN
2615-3017, 1978-7227
Published
2025-01-13
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development. Through IPKM modeling, factors that significantly influence regional public health development can be discovered. This research aims to find an appropriate model for modeling IPKM and determine the factors that significantly influence public health development. The data used is the 2018 IPKM data collected from 119 cities/regencies in Java. We propose three models namely logistic regression (LR), mixed logistic regression (MLR), and geographically weighted logistic regression (GWLR). The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria. Based on the MLR model, the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.

Collaboration network of the authors

40 authors · 121 co-authorship links · 4 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
  • Binus University
  • Universitas Halu Oleo
  • 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 University4
Muhammad Nur AidiAffiliation unknown3
Kusman SadikAffiliation unknown3
Bagus SartonoIPB University3
Aji Hamim WigenaAffiliation unknown2
Indahwati IndahwatiAffiliation unknown2
Anwar FitriantoAffiliation unknown2
Asysta Amalia PasaribuBinus University2
Budi SusetyoAffiliation unknown2
Gusti Arviana RahmanUniversitas Halu Oleo2
Arie PurwantoAffiliation unknown2
Muhammad Aziz SuprayogiAffiliation unknown2
Joao Ferreira Rendes Bean LolyAffiliation unknown2
Zein Rizky SantosoAffiliation unknown1
Agus Mohamad SolehIPB University1
Maulida Fajrining TyasAffiliation unknown1
Christiana Anggraeni PutriAffiliation unknown1
Husnun NashirAffiliation unknown1
Irvanal HaqAffiliation unknown1
Efriwati EfriwatiAffiliation unknown1
Dwi Agustin Nuriani SirodjAffiliation unknown1
Yudi Fathul AminAffiliation unknown1
I Made SumertajayaAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Siskarossa Ika OktoraAffiliation unknown1
Dariani MatualageAffiliation unknown1
Asysta Amalia PasaribuAffiliation unknown1
Nur Fitriyani SahamonyAffiliation unknown1
Ita WulandariAffiliation unknown1
Assyifa Lala Pratiwi HamidAffiliation unknown1
Pardomuan Robinson SihombingAffiliation unknown1
Erfiani ErfianiAffiliation unknown1
Alona DwinataAffiliation unknown1
Ade RiyawanAffiliation unknown1
Logananta Puja KusumaAffiliation unknown1
Dhea DewantiAffiliation 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">A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA</dc:title>
	<dc:creator>Setiawan, Erwan</dc:creator>
	<dc:creator>Suprayogi, Muhammad Azis</dc:creator>
	<dc:creator>Kurnia, Anang</dc:creator>
	<dc:description xml:lang="en-US">The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development. Through IPKM modeling, factors that significantly influence regional public health development can be discovered. This research aims to find an appropriate model for modeling IPKM and determine the factors that significantly influence public health development. The data used is the 2018 IPKM data collected from 119 cities/regencies in Java. We propose three models namely logistic regression (LR), mixed logistic regression (MLR), and geographically weighted logistic regression (GWLR). The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria. Based on the MLR model, the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.
&amp;nbsp;</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-01-13</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/12942</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss1pp129-140</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application; 129-140</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss1year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12942/9554</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Erwan Setiawan, Muhammad Azis Suprayogi, Anang Kurnia</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>