ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN METODE MUTLAK SIMPANGAN TERKECIL PADA PEMODELAN KEJADIAN DIARE DI KOTA SEMARANG

*Ika Chandra Nurhayati  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Agus Rusgiyono  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Hasbi Yasin  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Received: 12 Feb 2020; Published: 13 Feb 2020.
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Open Access Copyright 2020 Jurnal Gaussian
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

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Abstract

Diarrhea is one of many health issues in developing country like Indonesia, because the sickness and the death number are still high. According to health profile of Semarang City, the people who suffer from diarrhea from 2010-2015 are decreasing. The lowest point happened at the year 2013 with the total case of 38.001, however there are an increasing number from 2014-2015. The distribution data of diarrhea is a spatial data. The differences between environment and sanitation could cause spatial heterogeneity. The spatial heterogeneity could cause the produced variant value no longer constant, but instead it is different on each region. Therefore, regression model that involves the effects of spatial heterogeneity is needed, which are Geographically Weighted Regression (GWR) that is built by Weighted Least Square (WLS) adjuster. Although, GWR parameter adjuster that used WLS is very sensitive with the existence of outliers. The existence of the outlier in the data will create a huge residual. Thus, more robust method is needed, which is Least Absolute Deviation (LAD) methods in order to estimate the parameter on model GWR. This model is called Robust GWR (RGWR). The result shows that the model events of diarrhea on each region in Semarang City are different. Furthermore, the model events of diarrhea with RGWR model generate MAPE 16,3396% which means the performance of RGWR is formed well.

 

Keyword: Diarrhea, Robust, Geographically Weighted Regression, Least Absolute Deviation

Keywords: Diarrhea, Robust, Geographically Weighted Regression, Least Absolute Deviation

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