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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics Eksponensial STATISTIKA
M. Fathurahman
Universitas Mulawarman
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI

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Regresi Logistik Multinomial untuk Memodelkan Kombinasi antara Status IPKM dan Status IPM Kabupaten/Kota di Pulau Kalimantan Yusrian Paliling; M. Fathurahman; Sri Wahyuningsih
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 3 (2023): MAY, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i3.22299

Abstract

Multinomial Logistics Regression (MLR) is a regression model developed from the Binary Logistics Regression (BLR) model. The response variable of the RLM model has three or more categories and has a multinomial distribution, with the data scale being nominal. The response variable in this study is a combination of the Public Health Development Index (PHDI) status and the Human Development Index (HDI) status of districts/cities in Kalimantan Island, 2018, divided into four categories with category one as a comparison. The predictor variables used were the number of the public health center, the percentage of poor people, economic growth, the pure junior high school participation rate, and the percentage of the population with a minimum of junior high school education. The MLR parameter model was estimated using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The hypothesis testing of the MLR model was used by the Likelihood Ratio Test (LRT) method and the Wald test. The best model selection in this study uses the backward method, and the interpretation of the best MLR model uses the odds ratio value. The results showed that the best MLR model is a model that has three predictor variables. The factors that significantly influenced the combination of PHDI status and the HDI status of districts/cities in Kalimantan Island in 2018 were the percentage of poor people, economic growth, and the percentage of people with the minimum level of education in junior high school.
Co-Authors Abidin, Ahmad Aliful Andrea Tri Rian Dani Annisa Zuhri Apridayanti Arif, Zainul Astafira, Ilyas Dani, Andrea Tri Rian Darnah Darnah, Darnah Dini Elizabeth Fachrian Bimantoro Putra Hasanah, Siti Hadijah Ika Purnamasari Khairun Nida Khasanah, Lisa Dwi Nurul Meiliyani Siringoringo Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Murdani, Endah Mulia Ni'matuzzahroh, Ludia Ningsih, Eva Lestari Nurmayanti, Wiwit Pura Orvanita Putra, Fachrian Bimantoro Putri Aisha Putri, Ditha Reginna Rasidia, Fikri Regita Putri Permata Rito Goejantoro, Rito Roejantoro, Rito Rut Esra Sekar Nur Utami Sifriyani Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Sri Wahyuningsih Sri Wahyuningsih Surya Prangga Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Yusrian Paliling