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Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 12 Documents
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Search results for , issue "Vol. 12 No. 2 (2021)" : 12 Documents clear
Regresi Logistik dengan Metode Bayes untuk Pemodelan Indeks Pembangunan Manusia Kabupaten/Kota di Pulau Kalimantan Syafitri, Febriana; Goejantoro, Rito; Wasono, Wasono
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i2.802

Abstract

Human Development Index (HDI) is an indicator that can measure success in efforts to build the quality of human life. HDI is also a measure of the prosperity of a region which is observed based on three dimensions, namely health, education and economy. Based on HDI publication by the Central Statistics Agency in 2018, it showed that the scores of HDI for 56 districts/cities in Kalimantan Island only has two categories of HDI which are medium and high. Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution functions. The estimation with Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with Gibbs Sampler algorithm. The aim of this study is to examine the modelling of the factors that influence the HDI of districts/cities in Kalimantan Island and determine the accuracy of the model classification using logistic regression with Bayesian method. The data used is the HDI of districts/cities in Kalimantan Island in 2018. Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution functions. The estimation with Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with Gibbs Sampler algorithm. The results of modelling and analysis on districts/cities HDI data on Kalimantan Island showed that the factors that significantly influence HDI are the number of paramedic, the number of health facility and the participation rate of high school. The results of the classification accuracy of the model amounted to 82,14% which resulted in 37 districts/cities are categorized as the HDI medium category and 19 districts/cities are categorized as the HDI high category.
Model Geographically Weighted Poisson Regression (GWPR) dengan Fungsi Pembobot Adaptive Gaussian: (Studi Kasus : Angka Kematian Ibu (AKI) di 24 Kab/Kota Kalimantan Timur dan Kalimantan Barat Tahun 2017) Ridhawati, Ridhawati; Suyitno, Suyitno; Wasono, Wasono
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i2.807

Abstract

The Geographically Weighted Poisson Regression (GWPR) Model is a regression model developed from Poisson regression or a local form of Poisson regression. The GWPR model generates a local model parameter estimator at each observation location where the data is collected and assumes the data is Poisson distributed. The estimation of GWPR model parameters uses the Adaptive Gaussian weighting function by determining the optimum bandwidth using GCV criteria. Based on the GWPR model, it is found that the factors that influence the maternal mortality rate (MMR) data in 24 districts (cities) of East Kalimantan and West Kalimantan are the percentage of pregnant women receiving Fe3 tablets, pregnant women with obstetric complications and the number of hospitals. These three variables produce four groups of GWPR model. Based on the GCV value, it is obtained that the best model is the GWPR model because it has the smallest GCV value.

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