VISUALISASI DAN KLASIFIKASI MALWARE MENGGUNAKAN METODE K-NEAREST NEIGHBOR

HAFIZ, MEIDI DWI and Stiawan, Deris (2020) VISUALISASI DAN KLASIFIKASI MALWARE MENGGUNAKAN METODE K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

Visualization is a method used to represent data in the form of an image to display hidden information. The visualization in this study uses malware data to be converted into a grayscale image. This study uses 10 types of malware with a total of 1000 data. The test data is divided into training data as much as 80% of the test data is 20% of the total data. Malware is tested using Local Binary Pattern (LBP) to clarify grayscale. The results of classification using K-Nearest Neighbor (K-NN) with values of k = 1, k = 5, k = 10, k = 15, k = 20, k = 25 found an accuracy rate of 96.84%, a precision of 82.01% and F1 score of 81.50%. The results of applying the K-Nearest Neighbor (K-NN) algorithm for malware classification in the form of grayscale images have found very good results.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Visualization, Citra grayscale, Local Binary Pattern, K-Nearest Neighbor
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 9744 not found.
Date Deposited:	14 Jan 2021 03:51
Last Modified:	14 Jan 2021 03:51
URI:	http://repository.unsri.ac.id/id/eprint/39918

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