Skripsi

DETEKSI MULTI-CLASS CLASSIFICATION TERHADAP SITUS JUDI ONLINE MENGGUNAKAN METODE RANDOM FOREST

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Penilaian

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The rapid development of technology and internet access has fostered the widespread activity of online gambling, becoming a global phenomenon. This research aims to classify multi-class detection of online gambling sites using the Random forest method based on traceroute data. Data were obtained through extraction from traceroute activities conducted on gambling sites, followed by data preprocessing including cleaning, encoding, normalization, and balancing. The classification model was built and evaluated using performance metrics such as accuracy, precision, recall, and F1-score, with validation through a confusion matrix. The best results were achieved with an 80% train data, 20% test data ratio and 128 decision trees, reaching an accuracy of 97.9%. Additionally, this study also developed an ontology to visualize network hop paths of online gambling sites. The findings are expected to assist in automated and accurate identification and monitoring of online gambling activities.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003701	T176918	T1769182025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1769182025

Publisher

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

Collation

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

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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