Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails

In the ever-growing digital era, email spam is a serious threat that affects user productivity and information security. This study aims to analyze the comparative effectiveness of Naive Bayes and SVM algorithms with radial basis function (RBF) kernels in classifying spambots in emails. The methodology used includes collecting email datasets, applying both algorithms for classification, and evaluating performance using accuracy, precision, recall, and f1-score metrics. The results showed that SVM RBF performed better than Gaussian Naive Bayes, with significant improvements in all evaluation metrics. These findings provide important insights for the development of more accurate and efficient spam detection systems, and highlight the importance of selecting appropriate algorithms in the face of complex data classification challenges.