Published: 2024-12-01
The Application of Artificial Intelligence for Anomaly Detection in Big Data Systems for Decision-Making
DOI: 10.35870/ijsecs.v4i3.3358
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Abstract
The development of big data technology has generated huge volumes of diverse data, creating challenges in detecting anomalies that could potentially affect decision-making. This research aims to examine the application of artificial intelligence (AI) in detecting anomalies in big data systems to support faster, more accurate and effective decision-making. The approach used includes the integration of machine learning algorithms, such as classification-based detection, clustering, and deep learning, in identifying abnormal patterns in large datasets. The research method involves real-time dataset-based simulations by measuring the performance of AI models using accuracy, precision, recall, and F1-score metrics. The results show that the application of AI can significantly improve the anomaly detection capability compared to conventional methods, with an average accuracy of 92%.
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Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 3 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i3.3358
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Cut Susan Octiva
Universitas Amir Hamzah, Deli Serdang Regency, North Sumatra Province, Indonesia
Dikky Suryadi
STMIK Al Muslim, Bekasi Regency, West Java City, Indonesia
Loso Judijanto
IPOSS Jakarta Indonesia, South Jakarta City, Special Capital Region of Jakarta, Indonesia
Mitranikasih Laia
Universitas Nias Raya, South Nias Regency, North Sumatra Province, Indonesia
Dedy Irwan
Universitas Harapan Medan, Medan City, North Sumatra Province, Indonesia
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