Open AccessUtilizing Apriori Data Mining Techniques on Sales Transactions
Author(s) -
Umbas Krisnanto,
J Juharsah,
Purnama Putra,
Andini Dani Achmad,
Elkana Timotius
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19376
Subject(s) - database transaction , apriori algorithm , product (mathematics) , association rule learning , attractiveness , promotion (chess) , transaction data , set (abstract data type) , marketing , value (mathematics) , computer science , sales promotion , business , a priori and a posteriori , data mining , sales management , database , machine learning , mathematics , psychoanalysis , law , psychology , geometry , political science , programming language , politics , philosophy , epistemology
The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1Cre2), (Cre1Cre12) and (Cre9Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.