Analisis Klasterisasi Data pada Berbagai Bidang Menggunakan Algoritma K-Means: Studi Kasus dalam Kriminalitas, Ekonomi, Politik, Administrasi Digital, dan Perdagangan

Abstract

The K-Means algorithm is one of the most popular clustering techniques in data mining that is used to group data based on similarity of characteristics. This study aims to evaluate the application of the K-Means algorithm in five different domains: crime, digital administration, international trade, politics, and macroeconomics. Each case study uses specific datasets from reliable sources, such as BAPAS Purwokerto crime data, the level of digitization of institutions in Korea, Indonesia's export-import in 1988–1997, the results of political party elections, and Indonesia's macroeconomic indicators in 1980. The results of the analysis showed that K-Means was able to effectively group data and provide hidden patterns that are useful in the decision-making process in each field. Cluster quality evaluation was carried out using metrics such as the Silhouette Score and the Davies-Bouldin Index. This study confirms the flexibility of K-Means in handling data from various sectors and opens opportunities for the development of advanced clustering methods for multidomain analysis.