Abstract
Grouping sales data at the Secom Infotech Computer Store is still done manually in Excel. How to group it takes time and allows data to be lost. Clustering is one of the data mining methods that are unsupervised and K-Means is a non-hierarchical clustering method that attempts to divide existing data into one or more groups. The K-Means clustering method can be applied to classify a sales data based on the type of item, type of customer, number of items. The data used is sales data in January-June 2018 as many as 30 data. The results of the tests were carried out using the RapidMiner application where the results contained 2 clusters, namely cluster 0 totaling 14 data and cluster 1 totaling 16 data. K-Means clustering method can be used for data processing using the concept of data mining in grouping data according to attributes.
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