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Journal > INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS > Prediksi Website Pemancing Informasi Penting Phising Menggunakan Support Vector Machine (SVM)

 

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INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS
Vol 2 No 1 (2017): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS
Prediksi Website Pemancing Informasi Penting Phising Menggunakan Support Vector Machine (SVM)
Article Info   ABSTRACT
Published date:
01 Dec 2017
 
Abstrak: Perkembangan teknologi informasi dan komunikasi khususnya internet berdampak pada semua sektor kehidupan manusia tidak terkecuali dengan sektor perbankan dan keuangan. Selain memberikan dampak positif dengan dipermudahnya pelanggan dalam proses transaksi yang dapat dilakukan kapanpun dan  di  manapun  tanpa  dibatasi  oleh  ruang  dan  waktu  menggunakan  media internet, juga membawa potensi besar terhadap pihak-pihak yang tak bertanggungjawab untuk melakukan pencurian data dan informasi penting, salah satunya  dengan  teknik  phishing,  sehingga  metode  untuk  mendeteksi  serangan situs phishing memerlukan perhatian serius. Dalam penelitian ini penulis telah melakukan memberikan gambaran metode yang paling akurat untuk mendeteksi website phishing dengan membandingkan tiga metode antara lain Support Vector Machine, Naïve Bayes, dan Decision Tree menggunakan dataset publik dari UCI Machine Learning Repository (www.uci.edu) yang dioptimasi dengan feature selection dan diolah menggunakan program RapidMiner. Hasil penelitian menunjukan bahwa metode Decision Tree mempunyai tingkat akurasi sebesar 91,84%,  metode  Naïve  Bayes  sebesar  74,07%  dan  Support  Vector  Machine sebesar 92,34%.  Hal  ini  menunjukan  bahwa metode  Support  Vector  Machine mempunyai tingkat akurasi yang paling tinggi..
 
Kata Kunci: Decision Tree, Naïve Bayes, Phishing, Support Vector Machine
 
Abstract: 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 Machine
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