<![CDATA[This study aims to compare the performance of two machine learning algorithms for anomaly detection One-Class SVM and Isolation Forest in identifying anomalies in sea level data in Indonesia, a region with high tsunami risk. The data were obtained from an official Indonesian government source over a one-year period and underwent preprocessing, including data cleaning and standardization. The models were evaluated using statistical analysis (Mann-Whitney U test), clustering metrics (Davies-Bouldin Index and Silhouette Score), and visual inspection. The results indicate that Isolation Forest outperformed the other algorithm with a Davies-Bouldin Index of 0.8124, while One-Class SVM achieved the highest Silhouette Score at 0.4381, although its Davies-Bouldin Index was higher at 0.9163. This study contributes to the selection of effective algorithms for ocean monitoring systems as part of disaster mitigation strategies in Indonesia.]]>
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