Geographically Weighted Regression (GWR) Approach in the Modeling of Malnutrition and the Influencing Factors in Muna Regency

Malnutrition is a very alarming health problem in the international, national, inter-local and local area. Muna Regency is one of the regencies with the highest malnutrition case data in Southeast Sulawesi Province. The diversity of characteristics between regions determines the quality of health. Thus a significant factor to the response variable will certainly differ from one region to another. Therefore we need a statistical modeling method by taking into account the spatial aspect which is by using Geographically Weighted Regression (GWR) method. The Aim of this study to determine the modeling of malnutrition prevalence with GWR approach and the influencing factors in Muna Regency. The research method used in this research is non-reactive or unobtrusive method. Population and sample in this research is all data of malnutrition in Health Service of Muna Regency with 22 Districts as the unit of analysis. The results obtained showed that GWR model of malnutrition in Muna Regency obtained is 1.023651 + 0.001664X₃ + 0.032492X₄ + 0.010635X₆. The WGR model produces predictor variables that significantly influence the number of new cases of leprosy in Muna Regency, ie Percentage of Households using clean water sources (X₃), Percentage of Low Birth Weight (X₄) and Percentage of Under-fives who weigh Below the Red Line (BRL) (X₆). It is expected that further research should use variables that include local socio-cultural elements so that the expected outcomes are able to explain the local conditions of the area. besides, the researcher can use other more specific methods to get the maximum result.