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IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province)

*Alfi Fadliana  -  Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Raden Rahmat, Indonesia
Henny Pramoedyo  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Rahma Fitriani  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
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Keywords: Stunting; Multicollinearity; Geographically Weighted Regression; Locally Compensated Ridge

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