Skripsi

DETEKSI TRANSAKSI ANOMALI PADA BLOKCHAIN DENGAN MENGGUNAKAN METODE GATED RECURRENT UNIT (GRU)

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Penilaian

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Illegal activities such as money laundering using cryptocurrencies represented by Bitcoin have emerged. In this study, a Gated Recurrent Unit (GRU) neural network is used to identify anomalous transaction patterns in the dataset. The dataset is created by taking raw data based on yearly parameters to extract a subset of data. This subset represents data from the years 2011 to 2013 and is extracted by writing a Python code snippet. Due to the imbalance of the data, the dataset's classes are balanced using oversampling and undersampling techniques. The best model achieved an accuracy of 90.31%. Then, through k-fold validation, the model showed good consistency, with an average accuracy of 86.29%. These results indicate that the model has reliable performance in the detection task.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003712	T146293	T1462932024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1462932024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer., 2024

Collation

xiii, 62 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.207

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Prodi Sistem Komputer
deteksi anomali

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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