Penerapan Algoritma FP-Growth Data Mining Untuk Pole Persediaan Stok Barang Aksesoris HP
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Mayang Mughnyanti
*
Politeknik Negeri Medan, Medan,
Indonesia
(*) Corresponding Author
Abstract
The need for inventory stock, especially HP accessories, is one of the main pillars of the business process that must be carried out by the store management. Where the opportunity for calculation errors is carried out conventionally without an in-depth analysis that causes inaccurate determination of the amount of inventory that must be fulfilled, the results of the study present a solution with a Data Mining approach using association rule techniques. in the study using 100 data from sales transaction history within a certain period of time identified by running the Frequent Pattern Growth (FP-Growth) algorithm to maximize computational performance in the process of extracting item patterns. From the results of testing the stock data of HP accessories, it is known that the calculation results by applying Association rules in searching for each itemset by applying the FP-Growth algorithm there are 9 rules with the condition of a support value limit of <10% and a confidence value of 70%. While 16 rules that do not meet the value requirements of a total of 25 rules.
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