Eksplorasi Asosiasi Antara Fitur Sosial dan Ekonomi dalam Keputusan Pembelian di Platform E-Commerce Menggunakan Algoritma Apriori

Abstract

in consumer behavior, increasingly shaped by social and economic factors. This study aims to explore the associations between socio-economic features and purchasing decisions on e-commerce platforms by employing the Apriori algorithm, one of the most widely used data mining techniques for discovering product co-occurrence patterns. Utilizing a wholesale transaction dataset consisting of 38,765 entries, the research involved a series of data preprocessing steps, application of association rule mining, and visualization of product relationships using a graph-based network diagram. The analysis revealed that products such as whole milk, other vegetables, and rolls/buns appeared most frequently in transactions. Additionally, several product combinations with high lift and confidence values were identified, indicating strong potential for bundling and cross-selling strategies. The business implications of these findings include the development of association-based product recommendation systems to enhance promotional effectiveness and optimize product layout in online stores. Overall, this study underscores that leveraging the Apriori algorithm not only deepens the understanding of consumer behavior but also opens new opportunities for data-driven marketing innovation.