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Nia kurniati
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Minimarket Sales Optimization: Implementation of  FP-Growth dan MongoDB  With Python Nia kurniati; Novi Rukhviyanti
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2qh79f26

Abstract

This study applies an integrated FP-Growth algorithm with MongoDB and Python to analyze 150,000 minimarket transaction records over a one-year period. The dataset includes transaction numbers, product names, quantities sold, transaction dates, purchase prices, and selling prices. The parameters of a minimum support of 0.001, a confidence of 0.01, and a lift above 1.0 are used to ensure relevant association rules. The analysis indicates that the discovered product association patterns can increase operational efficiency by up to 15%, particularly in instant food and ready-to-drink beverage categories. These data-driven strategies also boost sales volume by 12.3% and reduce dead stock by 8.7%. Beras MCS 5KG stands out as the most profitable product, with a margin of IDR 1,066,724,400. The main strength of this study lies in the integration of FP-Growth with MongoDB, enabling large-scale real-time analysis without generating candidate itemsets. This approach enhances data processing efficiency, allowing minimarkets to optimise inventory and promotional strategies more accurately.
Co-Authors Novi Rukhviyanti