Segmentation Analysis of Countries Based on Human Development Index and Artificial Intelligence Readiness Using Unsupervised Learning Methods: Principal Component Analysis and K-Means Clustering

Abstract

This study explores the segmentation of over 120 countries based on indicators of human development and digital readiness using unsupervised learning methods. By applying Principal Component Analysis (PCA) for dimensionality reduction and K-Means Clustering for grouping, countries were categorized into four clusters reflecting different levels of development and AI preparedness. Indonesia was positioned within the developing cluster, highlighting both its digital growth potential and structural challenges in innovation and R&D. The analysis provides strategic insights for policy formulation and emphasizes the importance of digital inclusion, research investment, and international collaboration. The clustering approach offers an intuitive visual framework to understand global patterns in the era of technological transformation.