The Application of Artificial Intelligence for Anomaly Detection in Big Data Systems for Decision-Making - Octiva, Cut Susan; Suryadi, Dikky; Judijanto, Loso; Laia, Mitranikasih; Irwan, Dedy (Lembaga Komunitas Informasi Teknologi Aceh (KITA), 2024)
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%.