Analysis of Unemployment Patterns in Indonesia Using K-Means Clustering and Identification of Dominant Factors Using Random Forest - Yuliana; Rima Fazri Ramadhania; Asep Abdul Latip; Muhammad Farhan Harahapa (LPPM Politeknik Aceh Selatan, 2026)
The disparity in the unemployment rate between provinces in Indonesia, exacerbated by the COVID-19 pandemic, is the main focus of this study. This study aims to (1) map the grouping of unemployment patterns in 34 provinces based on the Open Unemployment Rate (TPT) time series data for the 2020-2024 period, and (2) analyze the most significant socio-economic determinants of the formation of these patterns. Applying a two-stage methodology, cluster analysis using K-Means—validated through the Elbow Method and Silhouette Score of 0.456—succeeded in classifying the provinces into four different groups. The two prominent clusters identified were