GEOGRAPHICALLY WEIGHTED REGRESSION ANALYSIS WITH ADAPTIVE GAUSSIAN IN THE SOCIAL AND ECONOMIC FIELDS FOR TUBERCULOSIS IN SOUTH SUMATRA 2020

Dia Cahya Wati(1*), Ismi Rizqa Lina(2)


(1) Department of Data Science, University of Insan Cita Indonesia
(2) Department of Data Science, University of Insan Cita Indonesia
(*) Corresponding Author

Abstract


Most of the tuberculosis germs not only attack the lungs, but can also attack other organs. Low income, population density, education level, low public health knowledge, and sanitation in the home environment are sources of transmission for tuberculosis sufferers. In this case, the number of tuberculosis cases varies between districts/cities. This study aims to analyze the factors that influence tuberculosis in South Sumatra using the Geographically Weighted Regression (GWR) approach. GWR is a modification of a simple regression model into a weighted regression model that can better explain the relationship between response variables and predictors. Tuberculosis is spread in every sub-district so that it can be identified more deeply in each sub-district using the GWR with an adaptive gaussian weighting function. Based on the results of the study, the distribution of tuberculosis was affected into 5 groups. The dominant group with economic influence is the average expenditure per capita (RPP) in the areas of Ogan Komering Ilir Regency, Banyuasin Regency, Ogan Ilir Regency, Palembang City and Prabumulih City and the coefficient of determination is 96.10834%.

Keywords


Tuberculosis; Geographically Weighted Regression (GWR); Adaptive Gaussian; South Sumatera.

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DOI: https://doi.org/10.26714/jsunimus.11.2.2023.22-30

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