KLASIFIKASI LALU LINTAS DARKNET MENGGUNAKAN METODE ADABOOST CLASSIFIER

SARI, RIZKY ELINDA and Stiawan, Deris and Afifah, Nurul (2024) KLASIFIKASI LALU LINTAS DARKNET MENGGUNAKAN METODE ADABOOST CLASSIFIER. Undergraduate thesis, Sriwijaya University.

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Abstract

Darknet dikenal karena kemampuannya yang dapat memberikan anonimitas yang sering digunakan untuk kegiatan ilegal. Laporan monitor keamanan dari BSSN menunjukkan bahwa 290.556 data kredensial akun dari instansi di Indonesia telah terekspos di darknet. Teknik klasifikasi merupakan pendekatan penting dalam mempelajari dan mengidentifikasi lalu lintas darknet. Penelitian ini mengusulkan AdaBoost Classifier sebagai metode dalam klasifikasi darknet. Penggunaan variasi dari n_estimator memberikan dampak yang signifikan terhadap hasil klasifikasi pada penelitian ini. Berdasarkan hasil evaluasi model AdaBoost menggunakan Confusion Matrix, performa model terbaik diperoleh pada nilai n_estimator 500 dengan akurasi 99,70%. Berhasilnya penelitian ini memberikan kontribusi dalam mengembangkan model klasifikasi dan evaluasi AdaBoost dalam hal klasifikasi darknet.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Klasifikasi, Darknet, AdaBoost, n_estimator, SMOTE-ENN
Subjects: T Technology > T Technology (General) > T1-995 Technology (General)
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Rizky Elinda Sari
Date Deposited: 27 Aug 2024 07:50
Last Modified: 27 Aug 2024 07:50
URI: http://repository.unsri.ac.id/id/eprint/156259

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