ARISTA, NATA and Stiawan, Deris and Afifah, Nurul (2023) KLASIFIKASI PDF MALWARE PADA LAYANAN AGREGATOR NASIONAL (GARUDA) KEMDIKBUD DIKTI DENGAN METODE NAIVE BAYES CLASSIFIER. Undergraduate thesis, Sriwijaya University.
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Abstract
Garba Rujukan Digital (GARUDA) merupakan salah satu e-library akademisi Indonesia yang memakai PDF sebagai ekstensi file. Dataset yang digunakan dalam penelitian ini berasal dari portal GARUDA dengan data sebanyak 10000 yang terdiri dari 9800 benign, 196 malicious html, dan enam malicious pdf. Dataset dianalisis menggunakan VirusTotal, PDF-parser dan PDFid. Proses klasifikasi dilakukan sebanyak tiga tahap menggunakan metode Gaussian Naive Bayes, yaitu: (i) klasifikasi pada dataset imbalance yang menghasilkan nilai akurasi sebesar 0,2280, (ii) klasifikasi dengan penambahan SMOTE untuk mengatasi dataset imbalance yang meghasilkan nilai akurasi sebesar 0,7361, (iii) klasifikasi dengan gabungan SMOTE dan Near Miss untuk mengatasi dataset imbalance yang menghasilkan nilai akurasi sebesar 0,9643, presisi 0,9640, recall 0,9675 dan f1score 0,9639. Kata Kunci : Klasifikasi, PDF Malware, VirusTotal, PDFid, Gaussian Naive Bayes, SMOTE, Near Miss.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | Klasifikasi Malware, Naive Bayes Classifier, SMOTE, Near Miss |
Subjects: | L Education > L Education (General) > L7-991 Education (General) Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
Depositing User: | Nata Arista |
Date Deposited: | 08 Aug 2023 06:50 |
Last Modified: | 08 Aug 2023 06:50 |
URI: | http://repository.unsri.ac.id/id/eprint/125111 |
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