Prediksi Website Pemancing Informasi Penting Phising Menggunakan Support Vector Machine (SVM)

The development of information and communication technologies, especially the Internet, have an impact in all sectors of human life with exception in the banking and financial sectors in addition to a positive impact to make essier customer in the transaction process that can do anytime and anywhere without being limited by space and time using the internet, it also brings great potential against parties not responsible for the theft of critical data and information, one of them  with  phishing  techniques,  so  the  method  for  detecting  a  phishing  site requires serious attention. In this study the authors try to give an overview of the most accurate methods to detect phishing websites to compare three methods such as Support Vector Machine, Naïve Bayes, and Decision Tree using public datasets from  the  UCI  Machine  Learning  Repository  (www.uci.edu)  optimized  with feature selection and processed using RapidMiner program that showed Decision Tree has a accuracy rate of 91.84%, Naïve Bayes method amounted to 74.07% and  Support  Vector  Machine  by 92.34%. Hereby declare  that  the  method  of Support Vector Machine has the highest degree of accuracy.   Keyword: Decision Tree, Naïve Bayes, Phishing, Support Vector Machin