<![CDATA[Abstract: The rapid development of information technology has intensified data exchange through email. However, this also increases the risk of spam distribution, which can compromise privacy, reduce productivity, and potentially pose security threats. This study aims to implement the Naïve Bayes algorithm to automatically classify emails into spam and non-spam categories. The Naïve Bayes method was chosen due to its ability to handle large datasets, efficient computational process, and high accuracy in text-based classification tasks. The research stages include collecting an email dataset, performing text preprocessing such as tokenization, stopword removal, and stemming, followed by training the model using the Naïve Bayes algorithm. The experimental results show that the developed model can classify emails with good accuracy and relatively short computation time.]]>
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