ANALISIS PENJUALAN PADA HAPPYMART MENGGUNAKAN ALGORITMA FP-GROWTH
DOI:
https://doi.org/10.46880/mtk.v12i2.5953Keywords:
Association Rule Mining, Data Mining, FP-Growth Algorithm, HappyMart, Market Basket Analysis, Sales AnalysisAbstract
Sales analysis is a crucial process for evaluating transaction data to understand consumption patterns and maximize business performance through a data-driven approach. HappyMart faces the challenge of significant transaction data growth, collecting a total of 1,500 transaction records during the period of August to October 2025. Inefficient manual analysis potentially triggers overstocking due to a lack of understanding of consumer purchasing patterns. This research aims to analyze purchasing patterns using the FP-Growth algorithm to formulate operational recommendations. The analysis stages include data collection, preprocessing, data transformation, and the extraction of association rules. System evaluation was conducted by comparing manual calculations in Excel, Python output, and RapidMiner. This experiment utilized a minimum support parameter of 0.2% and a minimum confidence of 60%. The research results identified product association patterns, where one of the strongest rules indicates: if consumers buy Terigu Kompas 1Kg and Aqua 1500ml, they will also buy Terigu Kompas 500G (support 0.2%, confidence 60%, and lift ratio 21.95). Practically, this highly correlated figure provides a direct contribution to the store in the form of recommendations for placing these products adjacent to each other in the same aisle, as well as implementing bundling promotion strategies to minimize stock accumulation.
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