CLUSTERING OF COUNTRIES BASED ON WORLD HAPPINESS INDICATORS USING K-MEANS - Fahira Puti Adylla; Dian Lestari; Yekti Widyaningsih (LPPM Universitas Negeri Jakarta, 2025)
Happiness is a multidimensional concept encompassing emotional well-being, life satisfaction, and perceived quality of life. The increasing use of happiness indicators as complementary measures of development beyond economic growth has attracted growing attention in statistical and applied research. This study aims to classify countries based on a comprehensive set of world happiness indicators using the K-Means clustering method. The indicators include the Happiness Index (subjective), gross domestic product (GDP) per capita, social support, healthy life expectancy, freedom to make life choices, generosity, negative perceptions of corruption, crime index, and cost of living. The optimal number of clusters is determined using the Silhouette Index, while Biplot analysis is employed to visualize cluster characteristics and relationships among indicators. The results identify three distinct clusters. Cluster 1 is dominated by countries with low happiness levels, Cluster 2 represents countries with moderate happiness profiles, and Cluster 3 consists of countries with high happiness levels. The findings demonstrate the effectiveness of multivariate clustering techniques in revealing structural patterns in happiness data and provide empirical evidence that may support comparative statistical analysis and policy-oriented applications.