The Application of Artificial Intelligence for Anomaly Detection in Big Data Systems for Decision-Making
DOI:
https://doi.org/10.35870/ijsecs.v4i3.3358Keywords:
Artificial Intelligence, Anomalies, Big DataAbstract
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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Copyright (c) 2024 Cut Susan Octiva, Dikky Suryadi, Loso Judijanto, Mitranikasih Laia, Dedy Irwan
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