CLUSTERING ANALYSIS WITH K-MEANS FOR ELECTRONIC SALES CLASSIFICATION IN BATAM IT STORE COM
DOI:
https://doi.org/10.22216/jit.v18i1.2678Keywords:
Clusters, Data Mining, Electronic, K-Means AlgorithmAbstract
This study aims to assist Batam IT Store Com in refining its sales categorization process and identifying patterns in product demand. The objective is to derive valuable insights from the store's sales data for future decision-making. The research employs the K-means Clustering method, which involves grouping data into distinct clusters through a calculation process. This results in the identification of products that are highly popular, best-selling, and those with low demand. The electronic sales data used for this study spans from March 2020 to November 2023. The findings reveal three clusters: Cluster 0 comprising 19 items, Cluster 1 with 2 items, and Cluster 2 with 4 items. In conclusion, the analysis and grouping facilitated by the K-means Clustering method prove beneficial for segmenting sales data at Batam IT Store Com. The outcome is a renewed understanding of product clusters that aids in analyzing and segmenting goods sold at the store.
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