Isolation Forest dengan Exploratory Data Analysis pada Anomaly Detection untuk Data Transaksi

  • I Made Sudarsana Taksa Wibawa Universitas Udayana
  • Anak Agung Istri Ngurah Eka Karyawati Universitas Udayana

Abstract

Managing value of data is one of the key aspects of presenting analysis for decision making support in various cases. One of such method is by managing detecting anomaly in the data. This research focuses on implementing Isolation Forest result of anomaly detection. This method is used on transaction dataset from Kaggle with about more than 500.000 records. The result this research shows that Isolation Forest used in the dataset have 0.899 in accuracy, 0.00649 in precision, 0.504 in recall, and 0.013 in F1 score.


Keywords: Isolation Forest, iForest, Anomaly Detection

Published
2023-07-17
How to Cite
TAKSA WIBAWA, I Made Sudarsana; KARYAWATI, Anak Agung Istri Ngurah Eka. Isolation Forest dengan Exploratory Data Analysis pada Anomaly Detection untuk Data Transaksi. Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 1, n. 3, p. 803-810, july 2023. ISSN 3032-1948. Available at: <https://ojs.unud.ac.id/index.php/jnatia/article/view/102719>. Date accessed: 22 nov. 2024.

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