Implementation of data mining to determine stock inventory at kenza grocery stores using the k-means clustering method
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Abstract
Toko Kenza is a store engaged in the sale of basic necessities, however, of the various kinds of groceries that are sold, not all of them are in demand, some are best-selling, best-selling and not best-selling. The data on sales and purchases of goods as well as unexpected expenses at this Kenza store are not well structured, so that the data only functions as an archive for the store and cannot be used for developing marketing strategies. Therefore it is necessary to apply data mining using the K-Means method at the Kenza store. The K-means method can be applied to Kenza stores to determine which basic food items are selling the best, selling and not selling. The application of the K-Means method to the Kenza store, namely by grouping the basic food stock data, then randomly selecting 3 clusters as the initial centroid. After the data in each cluster does not change, it can be seen that the end result is that there are 2 best-selling data, 15 data that are best-selling, 23 data that are not selling well. Then applying the K-Means method to Rapidminer is done by entering product stock data, namely initial stock, sold stock and final stock which will become a Database on MS. Check, the data is then connected to the RapidMiner tools and will be processed and formed K-Means. After that, RapidMiner will generate which products are the best selling, selling and not selling.
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