<![CDATA[This study aims to analyze and implement the Apriori algorithm to predict flood potential at the Lau Simeme Dam in Medan City by identifying association patterns among rainfall, water discharge, and water level parameters based on daily hydrological data. The main challenge of this study lies in the limitations of conventional mitigation systems, which are not yet capable of systematically and adaptively interpreting multivariate environmental relationships. The research method employs an empirical data mining-based approach, involving data preprocessing, numerical transformation into transactional data, frequent item set formation, and association rule derivation using minimum support and confidence parameters. The system was developed using Python and MySQL to support the operational analysis and visualization of prediction results. The results show that the Apriori algorithm is capable of generating consistent association patterns between heavy rainfall, increased water discharge, and flood alert status with an accuracy of 97.81%, precision of 100%, and recall of 97.81%. These findings indicate that association rule-based models possess interpretive and predictive capabilities relevant to supporting flood mitigation based on hydrological data.]]>
Copyrights © 2026
