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

HAMAS, M. RAFIE AL and Stiawan, Deris and Hermansyah, Adi (2025) DETEKSI MULTI-CLASS CLASSIFICATION TERHADAP SITUS JUDI ONLINE MENGGUNAKAN METODE RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_56201_09011182126018_cover.jpeg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (246kB) | Preview
	Text RAMA_56201_09011182126018.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_56201_09011182126018_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (6MB)
	Text RAMA_56201_09011182126018_0003047905_0030048909_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (334kB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011182126018_0003047905_0030048909_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (900kB) | Request a copy

Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Online gambling, Multi-Class Detection, Random forest, Traceroute, Ontology
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Rafie Al Hamas
Date Deposited:	07 Jul 2025 03:34
Last Modified:	07 Jul 2025 03:34
URI:	http://repository.unsri.ac.id/id/eprint/176918

Actions (login required)

View Item