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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

36 authors · 115 co-authorship links · 43 across different organisations

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

  • IPB University
  • Politeknik Statistika STIS
  • Universitas Suryakancana
  • Binus University
  • National Research and Innovation Agency
  • Bandung Islamic University
  • Universitas Mercu Buana Yogyakarta
  • 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
Khairil Anwar NotodiputroIPB University3
INDAHWATI INDAHWATIIPB University2
Asysta Amalia PasaribuBinus University2
Gusti Arviana RahmanUniversitas Halu Oleo2
Muhammad Aziz SuprayogiAffiliation unknown2
Arie PurwantoUniversitas Mercu Buana Yogyakarta2
Joao Ferreira Rendes Bean LolyNational University of East Timor2
Bagus SartonoIPB University2
Zein Rizky SantosoAffiliation unknown1
Aji Hamim WigenaIPB University1
Agus Mohamad SolehIPB University1
Maulida Fajrining TyasIPB University1
Christiana Anggraeni PutriPoliteknik Statistika STIS1
Anwar FitriantoIPB University1
Husnun NashirIPB University1
Muhammad Nur AidiIPB University1
Irvanal HaqIPB University1
Efriwati EfriwatiNational Research and Innovation Agency1
Dwi Agustin Nuriani SirodjBandung Islamic University1
Kusman SadikIPB University1
Yudi Fathul AminIPB University1
I Made SumertajayaIPB University1
Agus Sofian Eka HidayatPresident University1
Dian KusumaningrumUniversitas Prasetiya Mulya1
Siskarossa Ika OktoraPoliteknik Statistika STIS1
Dariani MatualageIPB University1
Nur Fitriyani SahamonyIPB University1
Pardomuan Robinson SihombingBadan Pusat Statistik1
ERFIANI ERFIANIIPB University1
Kristuisno Martsuyanto KapilukaIPB University1
Dhea DewantiIPB University1
Febryna SembiringIPB University1
Ajeng Bita AlfiraIPB University1

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 8 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>
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