KOMBINASI ALGORITMA PARTICLE SWARM OPTIMIZATION DAN K - NEAREST NEIGHBOUR UNTUK KLASIFIKASI WEBSITE PHISHING

EVIRZAL, MUHAMMAD FARHAN and Rini, Dian Palupi and Miraswan, Kanda Januar (2019) KOMBINASI ALGORITMA PARTICLE SWARM OPTIMIZATION DAN K - NEAREST NEIGHBOUR UNTUK KLASIFIKASI WEBSITE PHISHING. Undergraduate thesis, Sriwijaya University.

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Abstract

The k - Nearest Neighbor algorithm is a classification algorithm that can be used to classify phishing websites. However, there is a drawback when implementing the k- Nearest Neighbor algorithm, which is the value of k that is less than optimal and tends to be biased because the k values are obtained from manually determined experimental results to obtain optimal k values. Particle Swarm Optimization is an algorithm that is able to find optimal solutions because its focuses on solving optimization problems in the search for space to get solutions. So that with Particle Swarm Optimization the deficiencies in the k-Nearest Neighbor algorithm can be overcome. The results of the evaluation of phishing website classification with a combination of Particle Swarm Optimization and k-Nearest Neighbor are able to improve the average accuracy of the classification of phishing website that is 61.35% compared to the average accuracy of the classification of phishing website using only k-Nearest Neighbor which is only 56.13%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi website phishing, k – Nearest Neighbour, Particle Swarm Optimization
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 3840 not found.
Date Deposited:	27 Dec 2019 08:18
Last Modified:	27 Dec 2019 08:18
URI:	http://repository.unsri.ac.id/id/eprint/22459

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