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This is a Quantitative Study study.

September 17, 2025

Optimizing Socioeconomic Features for Poverty Prediction in South Sumatera

Key Points

Optimized feature engineering improved poverty prediction accuracy, indicating significant potential for socioeconomic interventions.
Among various models, Random Forest achieved R²=0.7244 and MAE=0.2489, confirming its effectiveness in poverty prediction.
Key predictors included composite indices like Education Index and Economic Index, revealing important socioeconomic interdependencies.
SHAP analysis identified Education and Economic Indices as top predictors, highlighting their relevance for targeted poverty reduction.

Abstract

Poverty in South Sumatera remains a complex challenge influenced by socioeconomic factors. Traditional methods often fail to capture nonlinear relationships critical for accurate prediction. This study enhances poverty prediction by optimizing feature engineering using 32-variable socioeconomic data from South Sumatra for the years 2019 to 2023. Data preprocessing included cleaning, imputation, normalization, and outlier handling. Feature aggregation created composite indices: Education Index (P1, P2, P3), Health Index (AH1–AH4), Economic Index (IE, GR, AI, EG), and Healthcare Workforce Index (HW1–HW9). Feature interaction derived ratios such as Income vs. Economy (AN/Education Index), Infrastructure vs. Health (road length/Healthcare Workforce Index), and Unemployment vs. Workforce (HI/AT), highlighting interdependencies. Dimensionality reduction (PCA) and Lasso Regression selected eight key predictors, including Year and Poverty Level. Among tested models, Random Forest performed best (R²=0.7244, MAE=0.2489). SHAP analysis identified Education and Economic Indices as top predictors. Optimized feature engineering improved model accuracy and interpretability, supporting targeted poverty reduction strategies in South Sumatera.

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