PERBANDINGAN KINERJA ALGORITMA k-NEAREST NEIGHBOR DAN MODIFIED k-NEAREST NEIGHBOR PADA KLASIFIKASI WEBSITE PHISHING

TRISNO, MOH and Primartha, Rifkie and Utami, Alvi Syahrini (2020) PERBANDINGAN KINERJA ALGORITMA k-NEAREST NEIGHBOR DAN MODIFIED k-NEAREST NEIGHBOR PADA KLASIFIKASI WEBSITE PHISHING. Undergraduate thesis, Sriwijaya University.

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Abstract

The k-Nearest Neighbor (kNN) algorithm is a classification algorithm that can be used to classify phishing websites, but the algorithm has many weaknesses such as the value of k bias, complex computing, memory limitations dan easily fooled by irrelevant attributes causes a low level off accuracy. This problem can be overcome by the development of kNN algorithm, namely Modified k- Nearest Neighbor (MkNN) where MkNN can overcome the problem of outliers in traditional kNN. So by using the kNN and MkNN Algorithms, it can be find the performance in the form of accuracy, recall, precision, the computing time and memory used. To find out its performance, the algorihtm will be using confusion matrix. The result shows that MkNN had an effect on increasing the value of accuracy, recall and precision compared to the kNN algorithm. The results of the evaluation of the performance of the algorithm using the best k value for both algorithms is (k = 5) so that MkNN is able to produce an accuracy of 95.60 % and the accuracy result using KNN of 95.35 % on the classification of phishing websites. But the inscrease also occurs in the computational time required by MkNN algorithm, which is greater than the KNN algorithm, whereas the memory used in the MkNN algorithm is less.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	k-Nearest Neighbor, Modified k-Nearest Neighbor, Phishing Websites, Classification, Accuracy, Recall, Precision.
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 6622 not found.
Date Deposited:	27 Jul 2020 05:06
Last Modified:	27 Jul 2020 05:06
URI:	http://repository.unsri.ac.id/id/eprint/31750

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