Geospatial Analysis of Stunting: A QGIS-based Case Study in Jember Regency - Efri Tri Ardianto; Alinea Dwi Elisanti; Rusdiarti Rusdiarti; Tegar Wahyu Yudha Pratama; Muhammad Abdul Rauf (International Journal of Healthcare and Information Technology, 2026)
Malnutrition, particularly stunting, remains a significant public health concern in Indonesia. In Jember Regency, the prevalence of stunting in 2024 reached 11.4%, which is notably higher than the East Java provincial average of 5.1%. Despite various intervention efforts, the reduction in stunting cases has not been significant, largely due to the lack of evidence-based approaches. This study aimed to analyze the spatial distribution of stunting cases using a geospatial approach. Data were obtained from the electronic Community-Based Nutrition Recording and Reporting System (e-PPGBM) of the Jember District Health Office for the years 2024–2025. The unit of analysis was 31 sub-districts in Jember Regency. Spatial analysis was conducted using Quantum GIS (QGIS) version 3.40.2. In 2024, 58% sub-districts were categorized as very high prevalence, 19.4% sub-districts as high, 19.4% sub-districts as moderate, and 3.2% sub-districts as low. In 2025, the distribution shifted, with 32.3% sub-districts categorized as very high, 25.8% sub-districts as high, 25.8% sub-districts as moderate, and 16.1% sub-districts as low. Trend analysis revealed that 12.9% sub-districts experienced an increase in stunting cases, 41.9% sub-districts remained unchanged, and 45.2% sub-districts showed a decline. Regarding the spatial relationship between regional distance and stunting incidence, urban and coastal areas tend to have fewer cases than highlands areas. Research findings indicate that this is due to limited access to animal protein sources, particularly seafood. Most residents rely on their own livestock products, such as eggs, chicken, and others. Prioritizing interventions in areas with high and very high prevalence rates while strengthening programs to sustain regions with low stunting cases, supported by enhanced geospatial analysis in nutritional surveillance to enable more effective stunting reduction strategies.