Landslides are a significant natural disaster threat in Indonesia, including Samarinda City. This study aims to evaluate the classification accuracy of landslides using the Naïve Bayes algorithm, combined with the Synthetic Minority Oversampling Technique (SMOTE) for class imbalance and Particle Swarm Optimization (PSO) for feature Selection and parameter optimization. Data were obtained from BPBD and BMKG Samarinda for 2022-2024, evaluated using 10-Fold Cross Validation and Confusion Matrix. The results show that SMOTE did not always improve model accuracy. Naïve Bayes without SMOTE achieved an average accuracy of 87.18%, while with SMOTE, it decreased to 70.55%. This indicates that SMOTE’s impact depends on the dataset. PSO, used for feature Selection and parameter optimization, gave mixed results. Naïve Bayes + PSO Feature Selection achieved an average accuracy of 68.25%, lower than the base model with SMOTE. Naïve Bayes + PSO Optimization achieved the best accuracy at 71.03%, though the improvement was minimal. The combination of Naïve Bayes + PSO Feature Selection + PSO Optimization had the lowest accuracy at 68.08%. This study emphasizes that the use of PSO for feature Selection and optimization needs careful consideration, especially when combined with SMOTE, as its effect on model performance is not always optimal.
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