Authors:

I Made Sudarsana Taksa Wibawa, Anak Agung Istri Ngurah Eka Karyawati

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”

Keywords

Isolation Forest, iForest, Anomaly Detection

Downloads:

Download data is not yet available.

References

References Not Available

PDF:

https://jurnal.harianregional.com/jnatia/full-102719

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://jurnal.harianregional.com/jnatia/id-102719. Date accessed: 08 Jul. 2024.

Citation Format

ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License