KLASIFIKASI PDF MALWARE PADA GARUDA KEMDIKBUD SEBAGAI AGREGATOR NASIONAL DENGAN METODE K-NEAREST NEIGHBOR

PASIN, TRI SHENA ORIVIA and Stiawan, Deris and Septian, Tri Wanda (2022) KLASIFIKASI PDF MALWARE PADA GARUDA KEMDIKBUD SEBAGAI AGREGATOR NASIONAL DENGAN METODE K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

Garba Rujukan Digital (GARUDA) is a digital service used for collecting archives of national publication documents in PDF format. There are sections in the PDF that can be used by hackers to carry out attacks that the file becomes PDF Malware. The dataset obtained is unprocessed data, which was analyzed using PDFiD to obtain features. The feature extraction results were used to classify multiclass labels, namely PDF-Malware, PDF-HTML, and PDF-Benign using K-Nearest Neighbor. The best K-Nearest Neighbor results are K=1 obtained a precision value of 98,2%, a recall of 98,4%, an f1-score of 98.3%, an accuracy of 98.3%, and K=3 obtained a precision value of 98%, recall of 98.4%, f1-score of 98.2%, accuracy of 98.3%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, PDF Malware, PDFiD, Multiclass, K-Nearest Neighbor
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Tri Shena Orivia Pasin
Date Deposited:	26 Dec 2022 05:46
Last Modified:	26 Dec 2022 05:46
URI:	http://repository.unsri.ac.id/id/eprint/84668

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