Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities Indonesian based on 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, Divisive analysis (Diana) are utilized compared achieve purpose. Clustering 90 Indonesia produced five optimal clusters. Furthermore, clustering results were validated using Silhouette width (SW) Dunn index (DI). showed k-means method most compact cluster. Hence, study's can be reference for determining steps of economic policy Indonesia.