Skripsi

DETEKSI TRANSAKSI ANOMALI PADA BLOCKCHAIN DENGAN MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM).

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Penilaian

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The rapid growth and anonymity offered by cryptocurrencies have made them vulnerable to being used for illegal activities. In this study, Long Short-Term Memory (LSTM) neural network is used to identify anomalous transaction patterns in the dataset. The dataset is created by retrieving raw data based on year parameters to extract a subset of the data. These subsets were extracted by writing python code snippets and represent data from 2011 to 2013. The dataset was class balanced by oversampling and undersampling techniques. The best model achieved an accuracy of 85.67%. Then, through k-fold validation, the model showed good consistency, with an average accuracy of 85.80%. These results indicate that the model has consistent and reliable performance in the given detection task.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307006722	T130851	T1308512023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1308512023

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiii, 64 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Penyimpanan Data Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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