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Sukma, Robby
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Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry NG, Wijaya; Sukma, Robby; Juliane, Christina
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.599

Abstract

This study leverages association rule mining to analyze transaction data from PT. Labda Anugerah Tekstil, a prominent player in the textile industry, to uncover significant purchasing patterns and associations between different fabric types. Utilizing data from January 1, 2022, to December 31, 2023, which includes 7,143 transaction entries, the research applies the FP-Growth algorithm followed by Association Rule Mining to identify and evaluate frequent itemsets and strong association rules within the dataset. The analysis revealed robust associations among fabrics such as Cotton, Linen, Rayon, and Viscose, suggesting substantial opportunities for targeted marketing strategies and inventory management enhancements. The findings indicate that strategically bundling and promoting associated fabrics can drive higher sales volumes and improve customer purchasing experiences. The insights from this study provide actionable strategies for optimizing marketing efforts and inventory management, aiming to enhance sales performance and customer satisfaction in the competitive textile market.
Co-Authors Christina Juliane, Christina NG, Wijaya