Association Rule Mining for Truck Body Damage Pattern Analysis Using Apriori and CRISP-DM - Dendy, Livanty Efatania; Tanamal, Rinabi (Politeknik Negeri Batam, 2026)
This study investigates damage patterns in truck body components by applying the Apriori association rule mining algorithm within the CRISP-DM framework. The analysis is based on 281 historical repair records from CV Lestari’s fleet throughout 2024. The dataset encompasses 14 attributes, including vehicle types, route categories, body materials, and load conditions. To ensure the robustness of the generated rules, parameter tuning was conducted using a grid search approach, resulting in minimum support and confidence thresholds of 15% and 60%, respectively. A total of 50 association rules were derived, with several rules demonstrating high confidence values and lift values above 1.1, indicating meaningful and non-random correlations. Notably, structural frame damage is strongly associated with mountainous routes and heavy loads, while door and hinge damage tends to occur in aluminum box-bodied trucks operating under medium loads. These patterns were aligned with practical insights from field technicians and further contextualized through technical recommendations, such as reinforcing high-stress points and adjusting inspection schedules for high-risk configurations. The findings support the formulation of predictive maintenance strategies, enabling companies to transition from reactive repairs to proactive, data-driven decision-making. By integrating rule-based insights into maintenance planning, the study contributes to reducing unexpected failures, optimizing inspection frequency, and enhancing overall fleet reliability.