PEMODELAN SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PNEUMONIA BALITA PROVINSI JAWA TENGAH - Putri Fajar Utami; Agus Rusgiyono; Dwi Ispriyanti (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2021)
Geographical and inter-regional differences have contributed to the diversity of child pneumonia cases in Central Java, so a spatial regression modelling is formed that is called Geographically Weighted Regression (GWR). GWR is a development of linear regression by involving diverse factors geographical location, so that local parameters are produced. Sometimes, there are non-local GWR parameters. To overcome some non-local parameters, Semiparametric Geographically Weighted Regression (SGWR) is formed to develop a GWR model with local and global influences simultaneously. SGWR Model is used to estimate the model of percentage of children with pneumonia in Central Java with population density, average temperature, percentage of children with severe malnutrition, percentage of children with under the red line weight, percentage of households behave in clean and healthy lives, and percentage of children who measles immunized. SGWR models on percentage of children with pneumonia in Central Java produce locally significant variables that is population density, average temperature, and percentage of households behave in clean and healthy lives. Variable that globally significant is percentage of children with severe malnutrition. Based on Akaike Information Criterion (AIC), SGWR is a better model to analize percentage of children with pneumonia in Central Java because of smallest AIC. Keywords: Akaike Information Criterion, Geographically Weighted Regression, Semiparametric Geographically Weighted Regression