Decision Support System for Prioritizing Extreme Poverty Alleviation at the Provincial Level in Indonesia Using K-Means and Random Forest AlgorithmsIldiaWatiauthorDecision Support System for Prioritizing Extreme Poverty Alleviation at the Provincial Level in Indonesia Using K-Means and Random Forest Algorithms Ildia Wati Computer Science Study Program, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor City, West Java, 16143, Indonesia Abstract Extreme poverty remains one of the major challenges in Indonesia’s national development agenda, with disparities in the severity of poverty across provinces making it difficult to objectively determine government intervention priorities. This study aims to develop a decision support system for identifying priority provinces for extreme poverty alleviation in Indonesia using a hybrid approach that combines K-Means Clustering and Random Forest. Panel data obtained from Statistics Indonesia (BPS) for the period 2016–2025 were utilized, comprising 34 provinces from 2016 to 2023 and 38 provinces from 2024 to 2025, resulting in 348 observations. Seven indicators were employed: poverty rate, Gini ratio, Human Development Index (HDI), Open Unemployment Rate (OUR), Economic Growth Rate (EGR), Poverty Gap Index (P1), and access to improved sanitation. K-Means Clustering was first applied to generate three objective priority labels (low, medium, and high), which were subsequently used as training labels for the Random Forest classification model. Model performance was evaluated using 10-fold Group Cross-Validation to prevent data leakage in the panel data structure, achieving an accuracy of 92.82%. Feature importance analysis revealed that the Poverty Gap Index (P1) was the most influential indicator (0.238), followed by the poverty rate and the Gini ratio, while the Economic Growth Rate contributed the least. The resulting model was implemented in a Laravel-based web application, with a pure PHP inference service to ensure that the classification results remained consistent with the model trained in Python. The proposed system provides an objective, data-driven decision support tool to assist policymakers and researchers in prioritizing extreme poverty alleviation programs across Indonesian provinces. Keywords: extreme poverty; K-Means clustering; Random Forest; decision support system; provincial prioritizatioIlmu Komputer2026-07Universitas Pakuan;Fakultas Matematika dan Pengetahuan AlamThesis