Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk

 (*)Rangga Yogasuwara Mail (Universitas Bina Darma, Palembang, Indonesia)
 Ferdiansyah Ferdiansyah (Universitas Bina Darma, Palembang, Indonesia)

(*) Corresponding Author

Submitted: September 25, 2022; Published: September 30, 2022

Abstract

Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.

Keywords


Sales; Data Mining; Association Rules; Frequent Itemset; FP-Growth; FP-Tree

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