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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Simantec Jurnal Edukasi dan Penelitian Informatika (JEPIN) Edukasi: Jurnal Pendidikan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Applied Business and Technology Reflection: Education and Pedagogical Insights PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Luxury: Landscape of Business Administration INOVTEK Polbeng - Seri Informatika
Marlim, Yulvia Nora
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Enhancement of Supervised Learning Models for Intrusion Detection Through Mutual Information and Hyperparameter Tuning Jollyta, Deny; Makaruku, Yoakhina Nicole; Hajjah, Alyauma; Marlim, Yulvia Nora
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5760

Abstract

Enhancing the performance of supervised learning algorithms through feature and hyperparameter testing remains challenging for users, particularly when detecting computer network intrusions. There are opportunities to assess whether a supervised learning algorithm performs optimally, depending on the number of features and the choice of hyperparameters. The purpose of this research is to enhance the network intrusion detection performance of three supervised learning algorithms, namely Support Vector Machine (SVM), eXtreme Gradient Boosting, and Random Forest, by using the Mutual Information feature selection approach and hyperparameter tuning. Mutual Information measures the dependency of features on the target. Features with high values are the most informative. Hyperparameters are not learned from the data; they are set before training begins. Hyperparameters are selected in accordance with the requirements of the three algorithms via iterative training and testing on the NSL-KDD dataset. The dataset was split into 80:20, 70:30, and 60:40. The results showed that the fifteen features with the highest mutual information were identified and trained on the data using appropriate hyperparameters. By splitting the data in an 80:20 ratio, the accuracy of Support Vector Machine reached its maximum, increasing from 90% to 98%. In contrast, eXtreme Gradient Boosting and Random Forest reached their maximum, increasing from 97% and 98% to 100%, respectively. The study’s findings advance our understanding of how algorithm performance depends on feature and hyperparameter selection.
Co-Authors Achmad Tavip Junaedi Adiya, M. Hasmil ahmad kamal, ahmad Alyauma Hajjah Cecilia, Cecilia Chandra Putra Dinata Dadang Priyanto Dalil, M Deny Jollyta Dewi Nasien Dwi Oktarina Erlin Erlin Fadrul, Fadrul Fauzan Azim Gusrianty Gusrianty Gusrio Tendra Gustientiedina Harry Patuan Panjaitan Hendri Hartono, Hendri Jahrizal Johan Johan Junadhi Junadhi Koto, Jaswar Laili Suryati Leonardo Yang Makaruku, Yoakhina Nicole Marcellino, Marcellino Muhammad Zarlis Musa, Sulaiman Nicholas Renaldo Nova Agustina Prihandoko . Rahmadian Yuliendi, Rangga Rahman, Sarli Ramalia Noratama Putri Saputra, Fandri Umar Faruq Veronica, Kristy Wahid, Nabila Wahyu Joni Kurniawan Wahyu Joni Kurniawan, Wahyu Joni Wilda Susanti William William Yuliendi, Rangga Rahmadian