International Journal For Multidisciplinary Research

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Sortive: Intelligent Product Grouping and Layout Recommendation Based on Transaction Data Using FP-Growth Algorithm

Author(s) Justine Delos Reyes Gravamen, Jerameel Osorio Udtujan, Jenny Rose Irish Resma Marasigan, Prof. Anna Liza O. Villanueva, Prof. Marvin H. Bicua
Country Philippines
Abstract Small to mid-scale supermarkets in the Philippines typically rely on intuitive product placement and supplier-based logistics, often overlooking latent purchasing patterns within their transaction data. This study developed Sortive, an intelligent system utilizing the FP-Growth algorithm to transform raw Point-of-Sale (POS) records from the JMFAITHHOPE Store into actionable spatial strategies. Following the Iterative Waterfall Model, the system architecture features a Decoupled Data Pipeline composed of four functional modules: User Access and Security, Data Ingestion and ETL, an FP-Growth Analytical Engine, and a Layout Optimization Dashboard. Technical evaluation by IT experts based on the ISO/IEC 25010 product quality model yielded a Grand Weighted Mean of 3.41 (Effective), with a peak score in Security (3.62) validating the robustness of the system's role-based access controls. User acceptance testing by store personnel resulted in a mean of 3.30 (Strongly Agree) for Functionality, specifically highlighting the value of narrated, data-driven placement insights (3.50). The findings prove that algorithmic Market Basket Analysis successfully replaces the traditional "Manual Loop" management practice with statistically-proven layout strategies, offering an affordable and scalable decision-support tool for local retail optimization.
Keywords FP-Growth Algorithm, Market Basket Analysis, Retail Optimization, ISO/IEC 25010, Data-Driven Decision Support, Philippine Retail Industry, Association Rule.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-26
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72212
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