<![CDATA[Fraud detection in online transactions is critical to protecting consumers and maintaining the integrity of the online business ecosystem. Dataset imbalance can affect the classification prediction performance. To overcome data imbalance, this research uses an oversampling approach with the SMOTE method. The aim of this research is to analyze the performance of the SMOTE algorithm and decision tree classification in dealing with data imbalance problems in fraudulent transactions. The dataset used is online payments taken from Kaggle. The dataset shows that there are unbalanced classes, and it was found that using the SMOTE method increased the performance value better than using it without the SMOTE method. Using SMOTE gets very high metric values, up to a recall value of 100%. This shows that the model used in classifying fraudulent transactions is very effective.]]>
Copyrights © 2024
