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PEMODELAN DATA GEOSPASIAL BALITA KURANG GIZI DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS | Oktarina | Jurnal Gaussian

DOI: https://doi.org/10.14710/j.gauss.13.2.%25p

PEMODELAN DATA GEOSPASIAL BALITA KURANG GIZI DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS

Cinta Rizki Oktarina  -  Department of Statistics, Universitas Bengkulu, Jl. WR. Supratman, Kandang Limun, Bengkulu City, Bengkulu 38371, Indonesia
*Idhia Sriliana  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
Esa Nur Fadhillah Sidik  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
Muhammad Akbar Firmansyah  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
Open Access Copyright 2024 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
This study examines the factors that influence under-five malnutrition in Indonesia using the Geographically Weighted Regression Principal Component Analysis (GWRPCA) method. The GWRPCA approach was chosen to overcome the problems of multicollinearity and spatial heterogeneity that often occur in geospatial data analysis. This study uses 10 factors that are thought to influence under-five malnutrition used as the dependent variable. Malnutrition in under-fives increases the risk of disease, death, and stunting, with data showing a caseload of 10.2% or 805,000 under-fives experiencing malnutrition. GWRPCA analysis successfully reduced data dimensionality and spatial heterogeneity by selecting four principal components that explained 81.4% of the total data variance, encompassing important information from the independent variables associated with under-five malnutrition. By using these principal components, the study was able to more efficiently identify the main determinants of undernutrition among children under five.
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Keywords: Malnutrition; Principal Components; Spatial Heterogeneity; Multicollinearity

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