Inventory Optimization through FP-Growth-Based Association Rule Mining of Material Stock Usage Patterns - Ubaidillah, Ubaidillah; Sumiati, Sumiati (Building of Informatics, Technology and Science (BITS), 2025)
This research focuses on analyzing the usage patterns of material stock at PT Baruna Energi Solusindo Teknik using Association Rule Mining (ARM) with the FP-Growth algorithm. The company often experiences material shortages or excess inventory due to manual and inaccurate stock planning. To address this issue, the study aims to discover frequent itemsets and association rules among materials used in water purification installations, enabling more data-driven procurement decisions. The research employs secondary data on material usage transactions from April 2024 to March 2025, which is processed using RapidMiner software. The FP-Growth algorithm identifies material combinations with high support and confidence values. For instance, Membrane RO has a support value of 75.9%, indicating that it is used in over three-quarters of all projects. Additionally, the combination {Membrane RO, PVC Pipe 1"} → SDL PVC 1" shows a confidence value of 83.3%, signifying a strong association among these items. The results suggest that stock optimization can be achieved by prioritizing frequently used items and associated combinations in procurement planning. This method not only improves inventory efficiency but also helps prevent stockouts and overstocking. The FP-Growth algorithm proves to be suitable and effective in identifying meaningful patterns in stock usage data.