ANALISIS KLASIFIKASI EMAIL SPAM MENGGUNAKAN ALGORITMA NAÏVE BAYES
DOI:
https://doi.org/10.33884/comasiejournal.v12i3.9792Keywords:
accuracy, class imbalance, naive bayes, smote, feature extractionAbstract
Spam emails pose a significant challenge in digital communication, requiring effective classification methods to enhance cybersecurity. This study evaluates the performance of the Naïve Bayes algorithm in detecting spam emails, focusing on accuracy, precision, and recall. The dataset consists of pre-labeled emails processed using TF-IDF for feature extraction. The results indicate that the algorithm achieved an accuracy of 90% before addressing class imbalance. After applying SMOTE, the final accuracy improved to 98%. These findings demonstrate that Naïve Bayes is an effective method for spam email classification, with SMOTE enhancing its performance in handling class imbalance.
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