Skripsi

VISUALISASI DAN KLASIFIKASI MALWARE MENGGUNAKAN METODE K-NEAREST NEIGHBOR

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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.

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Detail Information

Series Title

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Call Number

T399182021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xiii, 37 hlm,:ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Malware, Virus Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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