Abstract
Various types of smartphones come with different prices and specifications, causing sellers to sometimes struggle with providing recommendations to consumers who want to buy a smartphone that meets their desired specifications and price range. This challenge arises because it is difficult for sellers to remember the specifications of each smartphone for sale. K-Means Clustering aims to group existing specification data into several clusters, where the data in each cluster share similar characteristics. By forming these smartphone groups, it becomes easier for sellers to recommend appropriate smartphones to customers. The research results show that various smartphone brands are categorized into three groups: the Recommended Group, which includes 225 items; the Most Recommended Group, which includes 98 items; and the Less Recommended Group, which includes 27 items. This clustering is expected to help sellers easily increase the stock of recommended smartphones according to consumer needs in terms of price and specifications.
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