Penerapan Association Rule Mining Berbasis Algoritma Frequent Pattern Growth untuk Rekomendasi Penjualan - Didi Supriyadi; Ardelia Mahardika; Atik Febriani (JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020)
The level of competition and complexity of sales problems at retail companies, requires each retail company to be able to compete with other companies. One thing that can be done is through making decisions regarding sales that are more appropriate and effective. The amount of transactional data on retail company sales can be extracted useful information. The method that can be used to gather information is through the application of association rule mining. Association Rule Mining is a data mining method that focuses on transaction patterns by extracting associations or relationships of events. The market basket in a computerized retail company is the best way to provide scientific decision support support by determining the relationship between items purchased simultaneously in each transaction. FP-growth algorithm is used to determine the set of datasets that most often appear (frequent itemset) in a group of data. This research resulted in a minimum support value of 0.1% and a minimum value of 60% confidence in the number of rules produced amounted to 116457, a minimum value of 70% confidence in the number of rules produced amounted to 84086, and a minimum value of 80% confidence in the number of rules generated amounted to 48623 from the data processed in a number 22191. The results of this rule can be used for product marketing strategies. The minimum value of support is 0.1% where the greater the minimum value of confidence will result in fewer rules.