Penerapan Algoritma K-Means dalam Pengelompokan Indeks Harga Perdagangan Besar (IHPB)Produk Logam,Mesin, dan Perlengkapannya Tahun 2025 - Vio Br LumbanBatu; Virzinia Napitupulu; Sardo Pardingotan Sipayung (Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026, 2026)
The Wholesale Price Index (WPI) is an important economic indicator used to measure price changes at the wholesale level. The metal, machinery, and equipment product group plays a strategic role in supporting the industrial and national development sectors. Price fluctuations in this product group need to be analyzed systematically to identify their movement patterns. This study aims to classify the Wholesale Price Index (WPI) of metal, machinery, and equipment products in 2025 using the K-Means clustering algorithm. The data used in this study consist of annual WPI values obtained from the official publications of Statistics Indonesia (BPS). The research stages include data collection, data preprocessing, data normalization using the Min-Max method, determination of the optimal number of clusters, application of the K-Means algorithm, and analysis of clustering results. The number of clusters used is K = 3, representing low, medium, and high price index groups. The results show that the K-Means algorithm is effective in grouping WPI data based on the similarity of price index values. The clustering results provide a clearer overview of price movement patterns and can be used to support economic analysis, price monitoring, and policy decision-making in the industrial sector.