Implementation of FP-Growth and TOPSIS for Product Bundling Recommendation in Wholesale Stores
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Abstract
Discrepancies in inventory turnover rates between fast-moving and slow-moving items at Toko Murah Grosir have resulted in the accumulation of dead stock and inefficiencies in working capital. This study aims to develop a product bundling recommendation system by integrating the FP-Growth algorithm and the TOPSIS method. The analysis utilized a dataset comprising 605,914 historical transaction records. The procedure involved extracting inter-category association patterns using FP-Growth, classifying products based on quartile statistics, and ranking bundle candidates using the TOPSIS method, with criteria weights derived from the Analytic Hierarchy Process (AHP). Experimental results identified optimal parameters at a minimum support of 1% and a confidence of 20%, generating 4,110 potential bundle candidates. The TOPSIS method effectively prioritized recommendations, and User Acceptance Testing (UAT) indicated a recommendation relevance level of 86%. The integration of FP-Growth and AHP-TOPSIS demonstrates the potential to generate strategic bundling recommendations that assist in liquidating dead stock.
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