The Classification of Phishing Websites using Naive Bayes Classifier Algorithm - Roni Anagora, Roni Anagora; Rudini , Rudini; Rohmat Taufiq , Rohmat Taufiq; Ahmad Dedi Jubaedi , Ahmad Dedi Jubaedi; Rio Wirawan, Rio Wirawan; Arman Syah Putra (International Journal of Science, Technology & Management, 2022)
The background of this research is how to find out the selected websites which are classified through the naive Bayes classifier algorithm. With this algorithm, it can be seen how far the classification of Phishing is. The method used in this study is to use experimental methods or research on the data obtained, these tests will include new data that can be accounted for and can determine whether the data is suitable for use. The problem raised in this study is how to find out which principles have been clarified with the dizziness method using the naive Bayes classifier algorithm, with the algorithm, which websites can be classified properly.  The purpose of this study is how to find out the data tested through training data will produce new data, especially well for items and produce from values Naive Bayes algorithm obtained an average accuracy value of 92.98% with a TP Rate of 0.930%, FP Rate of 0.076%, Precision of 0.930%, Recall of 0.930% and Fmeasure of 0.930%.