Deteksi Situs Web Phishing Dengan Model Klasifikasi Menggunakan Metode K-Nearest Neighbor (KNN)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Ramadhan, Muhammad Adli
Siregar, Riki Ruli Affandi
Putra, Rakhmadi Irfansyah
Siregar, Riki Ruli Affandi
Putra, Rakhmadi Irfansyah
Subject
Teknik Informatika
Datestamp
2022-09-23 07:31:17
Abstract :
The development of information technology has made modern society deal more with products and services online. This creates opportunities for criminals to commit crimes, one of which is phishing. Phishing is a method used by internet criminals to deceive and steal user identities. Phishing websites are designed exactly the same as the original website in order to trick their victims (internet users) into pretending they are accessing web pages from legitimate sources. To detect phishing websites, a classification of phishing websites is carried out. The classification is carried out using twelve parameters to predict phishing websites. K-Nearest Neighbor is a data mining method used in classifying phishing websites. The application of the K-Nearest Neighbor method with k values using odd numbers, namely 3,5,7,9. Based on the test results, an average accuracy rate of 90% was obtained with the highest accuracy of 95% at the value of k = 3.
The development of information technology has made modern society deal more with products and services online. This creates opportunities for criminals to commit crimes, one of which is phishing. Phishing is a method used by internet criminals to deceive and steal user identities. Phishing websites are designed exactly the same as the original website in order to trick their victims (internet users) into pretending they are accessing web pages from legitimate sources. To detect phishing websites, a classification of phishing websites is carried out. The classification is carried out using twelve parameters to predict phishing websites. K-Nearest Neighbor is a data mining method used in classifying phishing websites. The application of the K-Nearest Neighbor method with k values using odd numbers, namely 3,5,7,9. Based on the test results, an average accuracy rate of 90% was obtained with the highest accuracy of 95% at the value of k = 3.
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