ANALISIS SPASIAL TINGKAT KESEJAHTERAAN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

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dc.contributor.authorReskia, Amanda
dc.contributor.supervisorHarison, Harison
dc.date.accessioned2022-07-27T03:01:50Z
dc.date.available2022-07-27T03:01:50Z
dc.date.issued2022-02
dc.description.abstractGeographically Weighted Logistic Regression (GWLR) is a method of combining Geographically Weighted Regression (GWR) with logistic regression which is applied to spatial data. The purpose of this research to determine model on community welfare using GWLR and find factors that influence the possibility of increasing the welfare status of each Province in Indonesia 2019. The variables used are total manpower, rate of GRDP, PMWand LFPR. In the response variable, the level of welfare as measured by the human development index (HDI) is in the binary category, namely 0 and 1 following the Bernoulli distribution. The results showed that the GWLR model with the Adaptive Gaussian Kernel function was better than the logistic regression model with the smallest Akaike Information Criterion (AIC) of 37.97.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.issnElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10603
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectHuman Development Index (HDI)en_US
dc.subjectGeographically Weighted LogisticRegression (GWLR)en_US
dc.subjectAkaike Information Criterion (AIC)en_US
dc.titleANALISIS SPASIAL TINGKAT KESEJAHTERAAN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSIONen_US
dc.typeArticleen_US

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