Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns

Jeffri Prayitno Bangkit Saputra, Silvia Anggun Rahayu, Taqwa Hariguna


The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).

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Data Mining; Association Rule; Market Basket Analysis; FP-Growth Algorithm

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Journal of Applied Data Sciences

ISSN : 2723-6471 (Online)
Organized by : Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia.
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