<![CDATA[The growth of e-commerce businesses has led to an increasing volume of sales transaction data stored in Online shops. However, this transaction data is often underutilized, even though it contains valuable product purchasing patterns that can support business decision-making. This study aims to apply the Apriori algorithm to identify product purchasing patterns in an Online shop using the Knowledge Discovery in Databases (KDD) method. The KDD framework is employed to ensure a systematic data analysis process, including data Selection, Preprocessing, data Transformation, application of the Apriori algorithm, and result interpretation. The data used in this study consist of Online retail transaction data obtained from the Kaggle platform, namely the Online Retail dataset, which represents real ecommerce transactions. Data processing is carried out using WEKA software. The analysis focuses on discovering frequent item sets and generating association rules based on minimum support, confidence, and lift ratio values. The results show that the Apriori algorithm can identify products that are frequently purchased together within a single transaction. These purchasing patterns can be utilized as a basis for marketing strategy recommendations, product bundling promotions, more efficient inventory management, and as support for developing product recommendation systems in Online shops.]]>
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