Penerapan Algoritma FP-Growth pada Data Penjualan Motor untuk Menemukan Kombinasi Produk yang Paling Laris
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Abstract
This study aims to apply the FP-Growth algorithm in analyzing motorcycle sales data to find the most frequently occurring purchase patterns and attribute combinations. The data used are motorcycle sales transaction data that includes attributes of motorcycle series, sales source, payment type, discount, and province. The analysis process is carried out through data preprocessing, frequent itemset formation, and association rules creation using the RapidMiner application. The results show that the Vario 160 series is the most dominant product in the association rules formed. Some of the main patterns generated include series = Vario 160 payment type = credit, series = Vario 160 discount = 0, and source = offline, series = Vario 160 payment type = credit with support values ranging from 0.106 to 0.127. In addition, a pattern of province = Central Java source = offline, discount = 0, payment type = credit was also found with the highest support value reaching 0.146. These patterns indicate that most motorcycle sales transactions are carried out offline, without discounts, and using the credit payment method. Based on these results, it can be concluded that the FP-Growth algorithm is effective in identifying consumer purchasing patterns and can be used as a basis for decision-making for motorcycle dealers in determining more targeted sales strategies, payment systems, and marketing planning.
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