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Bahari, Alwin
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Enhancing K-Nearest Neighbor Algorithm on Network Traffic Classification using Recursive Feature Elimination Amalia, Andika Elok; Bahari, Alwin; Bayuaji, Luhur
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.14277

Abstract

Network traffic classification is a category in network traffic monitoring analysis that has a very important function in network management analysis. By identifying the type of traffic flowing through a network, administrators can allocate bandwidth more effectively, maintain service quality, and detect abnormal activity, which makes it a fundamental part of network monitoring. One of the ways to do it is by machine learning both unsupervised and supervised. This study implemented supervised machine learning, namely K-Nearest Neighbor to network traffic dataset with two different schemas, with and without feature selection. These two schemas are compared directly so that any difference in results can be attributed to the feature selection process. It aims to clarify the impact of Recursive Feature Elimination to network traffic classification using K-Nearest Neighbor Algorithm. It classifies data with features to 17 target classes. Previous studies state that the use of feature selection in some techniques can improve its accuracy. Recursive Feature Elimination is chosen and result that the accuracy is increased from 0.66 to 0.76. Another thing that this study shows is that there is no pattern where the more data points from a class will result in better classification performance in that class. In other words, the number of samples available for a class does not guarantee that the class will be classified more accurately. This research clarifies that feature selection can improve classification performance. Selecting the most relevant features also reduces the amount of irrelevant information the algorithm must process, which makes the classification more efficient and reliable,
Co-Authors Amalia, Andika Elok Luhur Bayuaji, Luhur