Analisis Data Transaksi Konsumen Menggunakan Algoritma Apriori untuk Strategi Promosi pada Grosir Sembako - Vera Oktaviani; Joko Priambodo (TIN: Terapan Informatika Nusantara, 2026)
Wholesale grocery businesses generate an increasing amount of consumer transaction data, but such data has not been optimally utilized to identify purchasing patterns and relationships among products as a basis for developing promotional strategies. This condition means that promotional decisions may still be made based on general observations of frequently sold products, making it difficult to systematically identify opportunities from consumer purchasing patterns. This study aims to analyze the relationships among products in consumer transaction data using the Apriori algorithm and to generate promotional strategy recommendations in the form of bundling and cross-selling. The data consist of 350 consumer transactions involving nine product categories. The 350 transactions were used because they represent the entire transaction data available during the research observation period, thereby reflecting the transaction conditions of the research object during that period. Data processing was conducted using the Knowledge Discovery in Databases (KDD) stages, namely selection, preprocessing, transformation, data mining, and evaluation. The Apriori algorithm was applied with a minimum support of 30% and a minimum confidence of 70%. The analysis produced 9 frequent 1-itemsets, 21 frequent 2-itemsets, 3 frequent 3-itemsets, and 22 association rules that met the minimum thresholds. The rule with the highest confidence was Oil and Flour → Eggs, with a support of 48.57% and a confidence of 92.39%. The validation results showed that the manual calculations and system results produced consistent values. Based on the obtained association rules, promotional strategy recommendations can be directed toward bundling and cross-selling according to the confidence level of each rule. This study demonstrates that the Apriori algorithm can be used to identify purchasing patterns from wholesale transaction data and generate information that can support the development of data-driven promotional strategies. However, the results are limited to 350 transactions and do not directly measure the impact of implementing the promotional strategies on sales.