Abstract
The primary objective of this research is to classify spam, with the aim of mitigating the potential infringement on personal privacy resulting from spam. The categorization of spam messages involves the utilization of the Naive Bayes algorithm, which employs the principles of Bayes' Theorem and conditional independence assumptions to calculate and compare probabilities for classification purposes. The accuracy of spam categorization in the test is 98.3%, achieved by the implementation of Laplace smoothing and logarithmic accumulation techniques. The application of Naive Bayes for spam classification has several advantages, including the reduction of complexity associated with Bayes' Theorem, simplification of the procedure, decreased input requirements, and consistent classification accuracy and efficiency.