Abstract
The aim of this research is to evaluate the performance of a classification model on nonlinear data. The study utilizes accuracy, precision, sensitivity, and specificity metrics based on data from e-commerce X sellers. The classification model is developed using the Support Vector Machine (SVM) approach, employing different kernel functions such as linear, polynomial, and Radial Basis Function (RBF). By comparing the performance scores of each model, the best model is determined. The results indicate that the SVM model with a linear kernel outperforms the others, demonstrating the highest performance scores. This approach is applied to predict the status of sellers on e-commerce X.
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