AI-Based Models for Identifying Underdeveloped Villages in Indonesia's Rural Development

Abstract

This study improves the prediction and classification of underdeveloped villages in Indonesia using Artificial Intelligence (AI) and machine learning. It identifies key factors driving underdevelopment to inform policy interventions that support Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 10 (Reduced Inequality), and SDG 11 (Sustainable Communities). Using data from 75,261 villages based on Indonesia’s Village Development Index (IDM), the Decision Tree model achieved the highest classification accuracy at 99.5%. Analysis of feature importance revealed the Economic Resilience Index (IKE) as the most significant factor, followed by the Ecological Resilience Index (IKL) and the Social Resilience Index (IKS). These results align with the SDGs’ focus on economic, social, and environmental resilience. The research offers a data-driven approach to advancing rural development and guiding effective policy decisions in Indonesia.

Keywords

• machine learning
• artificial intelligence
• underdeveloped villages
• rural development