<![CDATA[Tsunami potential classification is an important task in disaster mitigation, and selecting the right machine learning algorithm plays a crucial role in building an accurate and reliable classification model. This study aims to compare the performance of Logistic Regression (LR) and Random Forest (RF) algorithms in classifying tsunami potential into multiple categories. The dataset used consists of 1,284 records with four selected features: Earthquake Magnitude, Latitude, Longitude, and Number of Runups. Three data splitting scenarios were applied: k-fold cross-validation (k=5), 70:30 split, and 80:20 split. Model performance was evaluated using accuracy, precision, recall, f1-score, and training time. The results show that Random Forest consistently outperformed Logistic Regression across all scenarios, achieving the highest accuracy of 0.6088, precision of 0.5751, recall of 0.6088, and f1-score of 0.5876 in the 70:30 scenario. However, Logistic Regression demonstrated significantly faster training time, averaging 0.02 seconds compared to 0.38 seconds for Random Forest. These findings suggest that Random Forest is preferable when classification accuracy is prioritized, while Logistic Regression is more suitable for time-critical applications]]>
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