APPLICATION OF DATA MINING IN GROCERY SALES USING THE FP-GROWTH ALGORITHM
Abstract
Sales of basic needs today require the use of information technology. The problem that often occurs is providing the best service to customers with the right business strategy to avoid business losses. Business actors are required to determine strategies that can increase sales of the products being sold. It is often found that transaction data is always increasing but is often not utilized properly. Another way is to determine a basic food sales strategy using data mining techniques. The technique used is the Fp-Growth Algorithm, which is an algorithm that generates frequent itemsets which will later be useful in the process of determining rules that can generate a choice. The Fp-Growth Algorithm is a development of the Apriori Algorithm. It's just that the Fp-Growth Algorithm uses tree development in searching for the types of goods that are often purchased. The data used are 26 types of basic food products and 30 transaction data. In this study, the minimum support value was 30% and the minimum confidence value was 60%.
Keywords:Â Groceries, Data mining, Frequent itemset, Fp-Growth.
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