Identify Rattan Sales Patterns Using the FP-Growth  Algorithm on CV. Busaeri Rattan

Robi; Nana Suarna; Irfan Ali; Dendy Indriya Efendi

doi:10.59934/jaiea.v4i2.877

Authors

Robi STMIK IKMI Cirebon
Nana Suarna STMIK IKMI Cirebon
Irfan Ali STMIK IKMI Cirebon
Dendy Indriya Efendi STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.877

Keywords:

FP-Growth, Data Mining, Sales Patterns

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.

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