A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA
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 influe...
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Universitas Pattimura
2025-01-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12942 |
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| author | Erwan Setiawan Muhammad Azis Suprayogi Anang Kurnia |
| author_facet | Erwan Setiawan Muhammad Azis Suprayogi Anang Kurnia |
| author_sort | Erwan Setiawan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a630ed36077f4d6586533a5b471ebafc |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-a630ed36077f4d6586533a5b471ebafc2025-08-20T04:01:47ZengUniversitas PattimuraBarekeng1978-72272615-30172025-01-0119112914010.30598/barekengvol19iss1pp129-14012942A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVAErwan Setiawan0Muhammad Azis Suprayogi1Anang Kurnia2Program Study of Mathematics Education, Faculty of Teacher Training and Education, Universitas Suryakancana, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaThe 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.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12942the public health development indexlogistic regressionmixed logistic regressiongeographically weighted logistic regression |
| spellingShingle | Erwan Setiawan Muhammad Azis Suprayogi Anang Kurnia A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA Barekeng the public health development index logistic regression mixed logistic regression geographically weighted logistic regression |
| title | A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA |
| title_full | A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA |
| title_fullStr | A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA |
| title_full_unstemmed | A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA |
| title_short | A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA |
| title_sort | comparison of logistic regression mixed logistic regression and geographically weighted logistic regression on public health development in java |
| topic | the public health development index logistic regression mixed logistic regression geographically weighted logistic regression |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12942 |
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