Skripsi

KOMPARASI DETEKSI ANOMALI LALU LINTAS JARINGAN PADA SITUS JUDI ONLINE MENGGUNAKAN RANDOM FOREST DAN DECISION TREE

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Penilaian

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This study discusses a comparison of the machine learning algorithms Random Forest and Decision Tree in detecting anomalies within network traffic on online gambling sites. The data was collected through network traffic capturing using Wireshark, followed by preprocessing stages that included data cleaning, encoding, balancing using Random Oversampling, and splitting into training and testing sets under several scenarios. The models were evaluated using accuracy, precision, recall, and F1-score metrics. In terms of efficiency, Decision Tree demonstrated shorter computation time, while Random Forest outperformed in terms of stability and detection accuracy. Feature analysis revealed that the Server Name Indication (SNI) in the TLS handshake process serves as a key indicator for detecting online gambling traffic, supported by other features such as destination IP, port, and network protocol. These findings highlight that Random Forest is more effective for high-accuracy anomaly detection, whereas Decision Tree is more suitable when computational efficiency is prioritized

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006189	T185471	T1854712025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1854712025

Publisher

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

Collation

xiv, 97 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

519.207

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Sistem Komputer
Teori Deteksi Anomali

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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