KLASIFIKASI SMS SPAM MENGGUNAKAN NAIVE BAYES DAN SVM
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Abstract
This research focuses on developing a framework for distinguishing spam messages in SMS using two main characterization strategies, namely Credulous Bayes and Backing Vector Machine (SVM). The need for effective methods to filter unwanted messages, especially spam messages that can disrupt the user experience, is increasing with the rapid growth of SMS communications. We collect and process various sets of SMS data for this research, including spam and non-spam messages. The features obtained from message text analysis are then used to train the model with the help of Naive Bayes and SVM classification techniques. After that, we use standard metrics such as accuracy, precision, recall, and F1 score to assess how well the two models perform.
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