Market Basket Analysis using FP-Growth Association Rule on Textile Industry
Abstract
Online shop is one of the technological developments that makes it easy for sellers and buyers to make transactions. A batch of data is produced from the process at an online shop and will be wasted if not utilized properly. An example is a batch of transaction data that may store hidden information that might benefit the online shop, such as information on customer transaction patterns or information on the relationship between two or more items that are most often purchased by customers. Based on that problem, this research conducts market basket analysis using one of the data mining methods, namely Association Rule with FP-Growth Algorithm, to find hidden information in a transaction data produced by an online shop. The data used in this research is an online shop transaction data that contains more than one item from November 2019 to December 2019 owned by one of the textile industries located in Ubud, Bali. The calculation of the FP-Growth Association Rule also tested using the Weka application. The results of market basket analysis are several associative rules related to the combination of best-selling items, such as “customers who buy Tigers Base Grey will have a 100% chance of buying Tribal Green too”. The result can be utilized by the textile industry owner to manage the layout of the online shop, add item recommendation features to the online shop based on items purchased by the customer, and also keep stock of the best-selling items to prevent running out of stock.
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References
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