DETEKSI ANOMALI TRANSAKSI BITCOIN DENGAN METODE ISOLATION FOREST

ANISKA, AGERA and Stiawan, Deris and Afifah, Nurul (2024) DETEKSI ANOMALI TRANSAKSI BITCOIN DENGAN METODE ISOLATION FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Bitcoin, introduced in 2008 by Satoshi Nakamoto, is the first digital currency to use blockchain for secure transactions. Despite its popularity, challenges in detecting illegal or suspicious transactions arise due to lack of regulation and anonymity. This research employs the Isolation Forest algorithm to identify fraudulent Bitcoin transactions. Isolation Forest was chosen for its superiority in detecting anomalies in large and heterogeneous datasets, with the ability to handle high-dimensional and large-scale problems. The method was tested on a dataset of Bitcoin transactions from a specific period, which was then divided into training and testing data. Evaluation results show consistent levels of accuracy, precision, recall, and F1-score, albeit with a tendency to classify normal transactions as anomalies. Model evaluation indicates the best performance with a training data split of 30% and testing data split of 70%, yielding an accuracy of 96.56%, precision of 98.25%, recall of 98.35%, and F1-score of 98.24%. The findings of this study make a significant contribution to the development of fraud detection systems for digital currencies, particularly in addressing security and anomaly issues commonly associated with Bitcoin transactions.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Bitcoin, Isolation Forest, Deteksi Anomali, Blockchain.
Subjects:	Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150-4380 Computer network resources
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Agera Aniska
Date Deposited:	13 Jun 2024 02:13
Last Modified:	13 Jun 2024 02:13
URI:	http://repository.unsri.ac.id/id/eprint/146836

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