Skripsi

DETEKSI ANOMALI TRANSAKSI BITCOIN MENGGUNAKAN METODE KMEANS DAN KMEDOIDS

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Penilaian

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Bitcoin is one of the most popular digital currencies widely used in online transactions. However, Bitcoin transactions are prone to anomalies, which may indicate suspicious activities such as fraud or other illegal actions. Detecting these anomalies is essential to enhance the security and reliability of the Bitcoin network. This study aims to identify anomalies in Bitcoin transactions using the KMeans and KMedoids clustering methods. The dataset consists of daily Bitcoin transactions from 2009 to 2024, sourced from BigQuery. The data preprocessing includes normalization using the Standard Scaler before applying the clustering algorithms. The resultsshow that the KMeans method achieved a silhouette score of 0.96, while the KMedoids method scored 0.98. Further analysis revealed that KMedoids is more reliable in handling data with outliers compared to KMeans, despite requiring longer computation times. Keywords: KMeans, KMedoids, Bitcoin, Anomaly, Clustering

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000560	T165835	T1658352025	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1658352025

Publisher

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

Collation

xv, 83 hlm.: ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

TUTI

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