<![CDATA[Cloud device-based information technology infrastructure generates large volumes of operational data that are dynamic and heterogeneous, increasing the complexity of monitoring and anomaly detection processes. Conventional rule-based approaches and supervised learning methods are often less effective due to limited labeled data and their inability to detect newly emerging anomaly patterns. Therefore, this study aims to apply and evaluate the Isolation Forest algorithm as an anomaly detection method for cloud device-based information technology infrastructure. The research data consist of system and network performance metrics, including CPU usage, memory utilization, disk activity, and network traffic collected from a cloud environment. The research stages include data preprocessing, normalization, and feature selection to improve data quality and model performance. The Isolation Forest algorithm is implemented using an unsupervised learning approach, where anomalies are identified based on the algorithm’s ability to isolate data points that exhibit characteristics deviating from the majority of normal data. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics, while parameter optimization is conducted using the grid search method to obtain the best configuration. The results indicate that the Isolation Forest algorithm is able to detect anomalies effectively, achieving high accuracy and a good balance between precision and recall. The model with optimal parameters demonstrates improved performance by reducing detection errors compared to the baseline configuration. Thus, the Isolation Forest algorithm can serve as a reliable and scalable solution to support monitoring activities and enhance the reliability of cloud infrastructure.]]>
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