<![CDATA[In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development]]>
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