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All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI)
Djabalul Lael, Tri Ahmad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology

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Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data Djabalul Lael, Tri Ahmad; Pramudito, Deskha Akmal
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 4 No 2 (2023): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v4i2.582

Abstract

This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.
Co-Authors Pramudito, Deskha Akmal