Sales System Using Apriori Algorithm to Analyze Consumer Purchase Patterns
DOI:
https://doi.org/10.36805/bit-cs.v3i1.2049Keywords:
sales, Data mining, Apriori algorithmsAbstract
This study aims to create a sales system to get order data on time, not too late to result in days, and the data becomes structured. As well as develop solutions to process sales transaction data which will increasingly use a priori algorithms to find out consumer buying patterns so that they can be output for decision making or knowledge. This study uses a qualitative method to deepen understanding of the phenomena currently happening as profoundly as possible. This shows the importance of depth and detail of the data studied. The system development uses the waterfall method because it fits perfectly with the needs of the system to be built. From the results of the study, calculating a sample of transaction data with a total of 12 data on August 7-8, 2021, using the Tanagra tools resulted in a rule association that if you buy a vortex, you will buy a Caraco with a support value of 58% and a confidence value of 100%, having a lift ratio value of 1.3 stated that the two products have a solid attachment to each other. Followed by if you buy Faraco, you will purchase a vortex. If you believe in a crystal, you will buy an arco that meets the specified parameter criteria with a minimum support value of 20% and minimum confidence of 50%.
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