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Desriwendi Desriwendi
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Journal : Jurnal Gaussian

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PEMODELAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) DENGAN FUNGSI PEMBOBOT FIXED GAUSSIAN KERNEL DAN ADAPTIVE GAUSSIAN KERNEL (Studi Kasus : Laju Pertumbuhan Penduduk Provinsi Jawa Tengah) Desriwendi Desriwendi; Abdul Hoyyi; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.734 KB) | DOI: 10.14710/j.gauss.v4i2.8403

Abstract

The Population Growth Rate (PGR) that are not controlled will have a negative impact on the various social-economic problems such as increased poverty, crime, and so forth. Factors contributing to the population growth rate of uncontrolled allegedly various between Regency/City. Geographically Weighted Logistic Regression (GWLR) is a local form of the logistic regression where geographical factors considered. This study will analyze the factors that affect the population growth rate of Central Java Province using logistic regression and GWLR with a weighting function of Fixed Gaussian Kernel and Adaptive Gaussian Kernel. The results showed that GWLR model with a weighting function of Adaptive Gaussian Kernel  better than logistic regression model and GWLR model with a weighting function of Fixed Gaussian Kernel because it has the smallest Akaike Information Criterion (AIC) value with the classification accuracy is 82.8 %.Keywords : PGR, Logistic Regression, Fixed Gaussian Kernel, Adaptive Gaussian Kernel, GWLR, AIC.
Co-Authors Abdul Hoyyi Triastuti Wuryandari