<![CDATA[Phishing websites are one of the most prevalent forms of cyberattacks and have the potential to cause significant losses, both financially and non-financially. Automatic phishing detection using machine learning algorithms has become an effective solution to address this threat. This study aims to classify phishing websites using the Extreme Gradient Boosting (XGBoost) algorithm and to address the issue of class imbalance by applying the Radial Based Undersampling (RBU) method. In addition, hyperparameter tuning was performed using the Random Search method to optimize the model's performance. The dataset used was obtained from the Kaggle platform and exhibits an imbalanced class distribution, where the number of non-phishing instances far exceeds phishing instances. This imbalance can lead to a biased model and reduce its ability to detect minority class patterns. Based on the evaluation results, the application of RBU significantly improved the model’s capability in detecting phishing instances, while hyperparameter tuning further enhanced its accuracy. The best model was achieved through a combination of RBU and Random Search, reaching an accuracy of 90.39% on the test data. These findings indicate that the combined approach of data balancing and model optimization provides an effective solution for phishing website classification and can be applied to similar cases in the field of cybersecurity.]]>
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