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Found 4 from your keywords: subject="deteksi anomali"
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DETEKSI ANOMALI PENDARATAN PESAWAT DI BANDARA SULTAN SYARIF KASIM II MENGGUNA…
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Noor, Taswiyah Marsyah

Accidents in commercial aviation in Indonesia have increased to 30% of total transport users by 2023. One important factor to reduce the risk of accidents and financial losses is compliance with landing procedures. The main objective of this analysis is to raise awareness of the importance of complying with these rules to minimise the risk of accidents and losses. Clustering techniques were app…

Edition
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ISBN/ISSN
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Collation
ix, 86 hlm.; ilus.; 29 cm
Series Title
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Call Number
T1597212024
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KOMPARASI DETEKSI ANOMALI LALU LINTAS JARINGAN PADA SITUS JUDI ONLINE MENGGUN…
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Aditya, Ar Rizky

This study discusses a comparison of the machine learning algorithms Random Forest and Decision Tree in detecting anomalies within network traffic on online gambling sites. The data was collected through network traffic capturing using Wireshark, followed by preprocessing stages that included data cleaning, encoding, balancing using Random Oversampling, and splitting into training and testing s…

Edition
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ISBN/ISSN
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Collation
xiv, 97 hlm.; ilus.; tab.; 29 cm.
Series Title
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Call Number
T1854712025
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DETEKSI ANOMALI TRANSAKSI BITCOIN DENGAN METODE ISOLATION FOREST
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Aniska, Agera

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…

Edition
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ISBN/ISSN
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Collation
xvii, 126 hlm.; ilus.; tab.; 29 cm
Series Title
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Call Number
T1468362024
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DETEKSI TRANSAKSI ANOMALI PADA BLOKCHAIN DENGAN MENGGUNAKAN METODE GATED RECU…
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Muhammad, Faisal Soultan

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 extrac…

Edition
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ISBN/ISSN
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Collation
xiii, 62 hlm.; ilus.; tab.; 29 cm.
Series Title
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Call Number
T1462932024