Implementation of Email Spam Detection Using Naïve Bayes Algorithm and Decision Tree J48 Text Mining Method

Email is quite popular as a digital communication media. This is because the message sending process via email is easy. Unfortunately, most messages in emails are spam emails. Spam is a message that the recipient of the message does not want because spam usually contains advertising messages or fraudulent messages. Ham is the message that the recipient wants. One way to sort these messages is to classify email messages into spam or Ham. Naïve Bayes and decision tree J48 are the algorithms that can be used to classify email messages. Therefore, this study aims to compare the effectiveness of the Naïve Bayes algorithm and decision tree J48 in sorting spam emails. The method used is text mining. Data containing the text of the email message in English will be processed before being classified with Naïve Bayes and decision tree J48. The pre-process stage includes tokenization, disposal of stop word lists, stemming, and attribute selection. Furthermore, Data text for email message will be processed using the Naïve Bayes algorithm and decision tree J48. The Naïve Bayes algorithm is a classification algorithm based on Bayesian Decision Theory, while the J48 decision tree algorithm is the development of the ID3 decision tree algorithm. The result of this research is that the decision tree J48 algorithm gets higher accuracy than the Naïve Bayes algorithm. The decision tree J48 algorithm has an accuracy of 93,117% while Naïve Bayes has an accuracy of 88,5284%. The conclusion of this study is that the decision tree J48 algorithm is superior to Naive Bayes for sorting spam emails when viewed from the level of accuracy of each algorithm