Open AccessImplementation of data mining with Apriori techniques to determine the pattern of purchasing of agricultural equipment (Case Study: XYZ Store)
Author(s) -
Supriyono Supriyono,
Kiki Farida Ferine,
Diana Puspitasari,
. Rulinawaty,
Elkana Timotius
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1933/1/012029
Subject(s) - apriori algorithm , purchasing , agriculture , computer science , data mining , data collection , order (exchange) , product (mathematics) , process (computing) , a priori and a posteriori , association rule learning , data science , operations research , business , marketing , engineering , mathematics , finance , ecology , philosophy , statistics , geometry , epistemology , biology , operating system
The aim of this research is to take advantage of data mining techniques using the Apriori sales data algorithm for agricultural equipment. This is due to the fact that the high potential agricultural sector in Indonesia has obstacles to the use of technology. Research was conducted in the district of Simalungun in a shop selling agricultural needs. Sales activities in the store continue and generate more and more data. In order to be of use to the resulting data, it is necessary to process these data with a certain algorithm that provides great benefits, in particular by maximizing the sales profits of agricultural products. Apriori Algorithm is one of the methods of data mining, the activities of which include data collection and the use of old data with the aim of finding regularities, patterns or relationships in data collection. The output of the algorithm can help future decision-makers, where one of the advantages is the rearrangement of the product layout, such as the most frequently sold products being assembled so that they are easily visible to consumers and can properly prepare stock items for the store.