Skripsi

VISUALISASI LALU LINTAS JARINGAN PADA SITUS JUDI ONLINE MENGGUNAKAN MACHINE LEARNING

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Penilaian

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The rapid growth of online gambling in Indonesia poses significant challenges insocial, economic, and network security aspects. This study aims to visualize networktraffic patterns of online gambling sites using machine learning approaches,specifically the K-Means and DBSCAN algorithms. Data were collected throughnetwork tapping with Wireshark during access to online gambling sites andapplications, then processed into a .CSV dataset. The research stages included datacleaning, normalization, feature selection, and clustering implementation. Theperformance evaluation employed Silhouette Score, Davies-Bouldin Index, andCalinski-Harabasz Index, with visualization conducted using Principal ComponentAnalysis (PCA). The results indicate that K-Means produced higher-qualityclusters with the best Silhouette Score of 0.683, while DBSCAN performed better indetecting outliers and irregular density patterns. Overall, this researchdemonstrates that machine learning-based visualization can effectively identifysuspicious communication patterns in online gambling network traffic and has thepotential to support the development of early detection systems in network security.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006194	T185475	T1854752025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1854752025

Publisher

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

Collation

xvi, 89 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

518.107

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Sistem Komputer
Teori Algoritma

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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