<![CDATA[Abstract.Network anomaly detection is a situation that occurs in network traffic that causes conditions to become abnormal. This research aims to analyze the performance of various machine learning algorithms in network anomaly detection and compare the performance of single classifier algorithms with ensemble learning. This ensemble learning technique has advantages such as increased accuracy and performance, can reduce the risk of overfitting and underfitting by using different subsets and features of data, and can turn weak learning into strong learning. However, on the other hand, this ensemble learning technique also has disadvantages in its use, namely that this ensemble method may not work well with high variance models, as the ensemble method may not be optimized for anomaly detection and that this method can be computationally expensive and time consuming due to the need to train and store multiple models. Some of the techniques used are deep learning, eager learning, lazy learning, bagging, feature selection, boosting, and stacking. In addition to this, this machine learning algorithm has weaknesses, including if any of the data used is incomplete, it will result in inaccurate completion data, making the programming process quite time-consuming. This research can help develop a more effective and efficient network anomaly detection system. The results of this research show that using ensemble learning and feature selection techniques can improve anomaly detection performance by reducing the processing time of redundant data and classification, as well as increasing precision values.]]>
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