ANALISIS REGRESI SPASIAL DAN POLA PERSENTASE KESEMBUHAN TUBERCULOSIS DI PROVINSI RIAU

Abstract

Spatial regression analysis is a statistical method that is useful for analyzing spatial data. Spatial analysis assumes the presence of spatial dependencies. One way to find out if there is a spatial dependency is to do a spatial autocorrelation test. Spatial autocorrelation is used to analyze the similarity of values at observation locations with neighboring locations on the same variable. There are several spatial models, including the Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM). The purpose of this study was to determine the best spatial regression model to model the percentage of tuberculosis cures in Riau Province and to determine the factors that influence it, by examining spatial autocorrelation using the Moran Index. Based on the results of the analysis by testing the Moran Index hypothesis, it was found that there was positive spatial autocorrelation and on examining spatial dependence, it was found that there was a lag dependence on the dependent variable, which means that the modeling was done with SAR. Based on the results of the SAR analysis, it was found that there were three independent variables that significantly influenced the percentage of TB cures, including the percentage of households with proper drinking water (𝑋1), the number of medical personnel at the puskesmas health facility (𝑋3) and the total number of public places (TTU) that met the requirements health (𝑋5).