Skripsi

DETEKSI TRAFFIC JARINGAN PADA AKTIVITAS JUDI ONLINE MENGGUNAKAN ALGORITMA EXTREME GRADIENT BOOSTING (XGBOOST)

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The rapid development of internet technology and the use of encryption in network traffic have led to an increase in online gambling activities that pose a threat to digital security. This study aims to detect network traffic indicative of online gambling by analyzing Server Name Indication (SNI) information during the TLS handshake process using the Extreme Gradient Boosting (XGBoost) algorithm. The data were processed through feature extraction, data cleaning, encoding, and data balancing using the Synthetic Minority Oversampling Technique (SMOTE). Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the XGBoost model is able to distinguish online gambling traffic from normal traffic in encrypted network environments with good performance, achieving an accuracy of 92.57%, precision of 88.85%, recall of 97.36%, and an F1-score of 92.91%. This study is expected to serve as a foundation for the development of machine learning-based online gambling detection systems to enhance network security. Keywords: Online Gambling, Network Traffic, TLS, Server Name Indication, XGBoost, SMOTE

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Detail Information

Series Title

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Call Number

T1878122025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 93 hlm.; ilus.; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

364.107

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Aktivitas Kriminal--Judi Online

Specific Detail Info

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Statement of Responsibility

MI

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