Dashboard Rekomendasi Bundling Produk Berbasis Algoritma Apriori Terintegrasi pada Sistem Point of Sale

Abstract

Competition in the minimarket retail business requires owners to implement data-driven promotional strategies rather than relying on intuition. Toko Sahabat, the case study of this research, holds a large volume of sales transaction data that has not been optimally utilized to support business decision-making, making it difficult for the owner to determine which products should be promoted or bundled together. This research aims to implement the Apriori algorithm into a product bundling recommendation dashboard integrated within a web-based Point of Sale (POS) application. The system was built using the Laravel 11 framework and MySQL 8.0 database, using 14,166 transactions collected from December 1, 2025, to June 20, 2026. The Apriori algorithm was used to identify frequent itemsets and generate association rules based on support, confidence, and lift ratio values. Using a minimum support of 2% and minimum confidence of 30%, the system generated 17 valid association rules, all with lift ratio values greater than one, the strongest being between Gula Pasir Gulaku and Kopi Kapal Api (confidence 90.27%, lift 5.42), which were mapped into five ready-to-use bundling package recommendations for shelf arrangement and promotional strategies. Black box testing across eight scenarios achieved a 100% success rate, confirming that all system functions operate as designed and are ready for operational use. The scientific contribution of this research lies in the direct integration of the Apriori algorithm into an active POS system, enabling bundling recommendations to be generated automatically and continuously from real transaction data, in contrast to prior studies that typically present association analysis results manually and separately from the store's operational system.