<![CDATA[Poorly planned drug inventory management often leads to imbalances between patient needs and the availability of medicines in clinics. This issue generally arises because transaction data has not been optimally utilized as a basis for decision-making. The purpose of this study is to identify patterns of drug associations by applying Association Rule Mining techniques using the Equivalence Class Transformation (ECLAT) algorithm. The research adopts a quantitative approach, utilizing one year of drug transaction data. The analysis reveals several combinations of medicines that are frequently prescribed together by healthcare providers. These association patterns provide valuable insights into prescribing tendencies within the clinic. By understanding the most common combinations, managers can plan drug procurement more accurately and efficiently. The information obtained not only helps anticipate the risk of stock shortages but also prevents excessive inventory that could result in waste. Thus, the application of the ECLAT algorithm proves effective in enhancing drug inventory management. Furthermore, the findings of this study can serve as a foundation for developing more efficient procurement strategies, ultimately improving the quality of healthcare services in clinics. Overall, leveraging transaction data through Association Rule Mining contributes significantly to evidence-based decision-making. This demonstrates that integrating data analysis techniques with inventory management can create a healthcare system that is more responsive, efficient, and patient-centered.]]>
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