Determining and Managing Stock of Goods Based on Purchasing Patterns Using the Frequent Pattern Growth Algorithm

  • Muhammad Aldi Zarkhasy Universitas Bumigora
  • Christofer Satria Universitas Bumigora
Keywords: Association Rules, Data Mining, FP-Growth Algorithm, Supermarket


Stock inventory is one of the important work activities, because stock inventory is the main element in the field of commerce. Mistakes regarding stock inventory will result in fatal problems, especially when stock inventory management is still done manually or improper stock inventory planning can cause the amount of stock to pile up due to the small amount of demand from consumers, resulting in the stock being damaged, especially those contains elements of expiration because it is not sold. On the other hand, if the stock of goods is low while consumer demand is high, consumers will turn to other supermarkets to look for the goods they want. This can make supermarkets lose money because they cannot meet consumer needs. Therefore, a system is needed to look for product item combination patterns using association techniques in determining and managing stock. This research aims to look for product item combination patterns in previous period transaction data based on purchasing patterns in determining and managing inventory at supermarkets using the FP-Growth method. Where with Min. Support 30% and Min. Confidence 70% produces 12 rules then with Min. Support 45% and Min.Confidence 60% produces 6 rules. Based on the comparison and analysis results obtained using the FP-Growth method, in this study the Min limit was chosen. Support 30% and Min. Confidence 70% due to generating 12 association rules. This information can be used as a reference for supermarkets in making decisions in determining and managing stock of goods based on purchasing patterns.


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How to Cite
M. Zarkhasy and C. Satria, “Determining and Managing Stock of Goods Based on Purchasing Patterns Using the Frequent Pattern Growth Algorithm”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 3, no. 1, pp. 1-10, Nov. 2023.

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