Application Of The Apriori Algorithm In The Point Of Sale (POS) System For Analyzing Consumer Purchase Patterns At Rahayu Motor Workshop

Abstract

Rahayu Motor Workshop faces sales optimization constraints due to unintegrated daily transaction data that merely accumulates as conventional archives. This lack of insight prevents the manager from predicting consumer purchasing patterns, leading to inventory overstock or stockouts. This study aims to develop a web-based Point of Sale (POS) system integrated with a data mining module using the Apriori Algorithm to identify item correlations. The system was built using the Rapid Application Development (RAD) method, utilizing CodeIgniter 4 and MySQL. The core engine employs Association Rule techniques to calculate support and confidence metrics from historical transaction histories. Functional evaluations via Black Box Testing confirmed that all modules operate validly. During the cutover phase, the system precisely extracted customer purchasing habits. This analytical output delivers actionable business intelligence for the workshop owner to design effective product bundling promotions, optimize shelf layouts, and streamline spare parts inventory control.