Open AccessASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH
Author(s) -
Delila Melati,
Titi Sri Wahyuni
Publication year - 2020
Publication title -
voteteknika
Language(s) - English
Resource type - Journals
ISSN - 2716-3989
DOI - 10.24036/voteteknika.v7i4.106499
Subject(s) - association rule learning , affinity analysis , database transaction , transaction data , data mining , computer science , value (mathematics) , revenue , database , machine learning , business , finance
Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth