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

  • Erwan Setiawan Program Study of Mathematics Education, Faculty of Teacher Training and Education, Universitas Suryakancana, Indonesia https://orcid.org/0009-0003-7943-3422
  • Muhammad Azis Suprayogi Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0000-0002-9860-4608
  • Anang Kurnia Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0000-0001-9409-2361
DOI: https://doi.org/10.30598/barekengvol19iss1pp129-140
Keywords: The Public Health Development Index, Logistic Regression, Mixed Logistic Regression, Geographically Weighted Logistic Regression

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.

 

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Published
2025-01-13
How to Cite
[1]
E. Setiawan, M. A. Suprayogi, and A. Kurnia, “A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA”, BAREKENG: J. Math. & App., vol. 19, no. 1, pp. 129-140, Jan. 2025.
Issue
Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Erwan Setiawan, Muhammad Azis Suprayogi, Anang Kurnia

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