Application of Data Mining to Predict Product Sales Using the K-Means Method

Authors

    Titania Delfiano( 1 ) Desiyanna Lasut( 2 )

    (1) Buddhi Dharma University
    (2) Universitas Buddhi Dharma

Keywords:


Clustering, Data Mining, K-means Algorithm, Predict Future Sales, Sales

Abstract

Sales activities that run every day generate large amounts of transaction data which can become data stacks. This also happened in a bag-selling shop called Toko Blessing. Toko Blessing is a bag sales business that focuses on children's school supplies with various categories such as school bags, drink bottle bags, and lunch box bags which have several variants of models and motifs in each category. With so many product variations and manual reporting, Toko Blessing faces difficulties in determining which products are best-selling and need to be added in large quantities to meet buyer demands and avoid the accumulation of less desirable products. With the availability of large sales data, if processed properly this data can be used to design the right business strategy. The K-means method is used because it makes it easier for the store to analyze and classify data to find out the level of the product through large amounts of sales transaction data that can be done quickly. The K-means method aims to determine sales patterns by looking at Blessing Shop sales transactions to help find out which products are often sold/best-selling and to predict future sales. From the data mining application using the K-means method, sales reports were generated based on sales transaction data from January 2021 to December 2022 totaling 1,188 data, which can later be used to assist Blessing Stores in making decisions on which products are superior to predict sales in the coming year.

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Published

2023-12-28

How to Cite

[1]
T. Delfiano and D. Lasut, “Application of Data Mining to Predict Product Sales Using the K-Means Method”, bit-Tech, vol. 6, no. 2, pp. 103–109, Dec. 2023.

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Vol. 6 No. 2 (2023): bit-Tech

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