Spatial Analysis and Determinants of Stunting Prevalence in Maluku Province: Multiple Linear Regression and GIS Approach
Abstract
This study aims to analyze the determinant factors of stunting prevalence in Maluku Province and map its spatial distribution patterns using a Geographic Information System (GIS) approach. Utilizing the most recent 2024 secondary data from the Central Bureau of Statistics (BPS), the variables tested include the percentage of underweight, poverty, access to decent water, access to decent sanitation, and Uninhabitable Houses (RTLH). The analysis methods applied are Multiple Linear Regression for partial influence testing and Moran's Index (GeoDa) for spatial autocorrelation analysis. Furthermore, spatial visualization was conducted using the Quantile 3-Class classification method to objectively identify high, moderate, and low-risk areas. The regression results indicate that partially, only the Access to Decent Sanitation variable has a significant and negative effect on stunting prevalence (p = 0.029 < 0.10), while other variables showed no significant influence in this model. Spatially, a weak negative autocorrelation pattern (Moran's Index = -0.191) was found, indicating that stunting distribution in Maluku tends to be random and influenced by local characteristics on each island. The novelty of this research lies in the integration of current post-pandemic data and spatial analysis in an archipelagic context, providing strategic recommendations for local governments to prioritize sanitation infrastructure interventions in coastal areas to accelerate stunting reduction in Maluku. Spatial autocorrelation or a tendency towards random distribution patterns. However, the Local Indicators of Spatial Association (LISA) results identified one region as a High-Low outlier, namely an area with a high prevalence of stunting surrounded by areas with a low prevalence. The conclusion of this study is that increasing access to adequate sanitation is the most crucial factor in reducing stunting rates in Maluku Province. Policy interventions are recommended to prioritize improvements to sanitation infrastructure, particularly in areas identified as spatial outliers.
Keywords: Stunting, Maluku, Spatial Analysis, Sanitation, GIS
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