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Willy Permana Putra
Politeknik Negeri Indramayu
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OPTIMASI HYPERPARAMETER XGBOOST UNTUK KLASIFIKASI INTRUSI MULTI-KELAS BERDASARKAN KINERJA PREDIKTIF DAN EFISIENSI KOMPUTASI Willy Permana Putra; Muhammad Edi Iswanto; Ihsan Doni Irawan; Renol Burjulius; A Sumarudin; Arif Maulana Yusuf
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7870

Abstract

The increasing number of network attacks requires intrusion detection models that are not only accurate but also efficient in terms of execution time, energy use, and carbon emissions. This study evaluates baseline XGBoost and four hyperparameter optimization approaches, namely Tree-structured Parzen Estimator (TPE), Asynchronous Successive Halving Algorithm (ASHA), Hyperband, and Particle Swarm Optimization (PSO), for multi-class intrusion classification on the CICIDS2017 Wednesday-workingHours subset. The evaluation uses predictive metrics, including accuracy, macro recall, macro F1-score, macro precision, and one-vs-rest AUC, as well as computational sustainability metrics consisting of CO2 emissions, energy consumption, execution time, and CPU/RAM/GPU energy details. The results show that XGBoost+TPE achieves the highest predictive performance with an accuracy of 0.9995 and macro F1-score of 0.9959, although its execution time increases to 13.04 seconds. In contrast, XGBoost+Hyperband maintains an accuracy of 0.9994 and macro F1-score of 0.9957 while reducing CO2 emissions by 56.50%, energy consumption by 56.54%, and execution time by 49.60% compared with the baseline. These findings indicate that Hyperband provides the most balanced configuration for intrusion detection scenarios that require high accuracy and computational efficiency.
Analysis of Machine Learning Models Based on Predictive Performance, Energy Consumption, and Carbon Emissions Willy Permana Putra; Eko Marpanaji; Septafiansyah Dwi Putra
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1482

Abstract

This study aims to evaluate intrusion detection models by jointly considering predictive performance and computational sustainability. The main problem addressed is that many intrusion detection studies emphasize classification accuracy while providing limited evidence about execution time, energy use, and carbon dioxide-equivalent emissions, even though these factors affect repeated training and practical deployment. The contribution of this work is a comparative assessment of four supervised learning models, Random Forest, Histogram-based Gradient Boosting, Support Vector Machine, and Extreme Gradient Boosting, under a unified experimental workflow. The methodology uses the Wednesday working-hours subset of the Canadian Institute for Cybersecurity intrusion detection dataset released in 2017, which contains benign traffic and several denial-of-service attack classes. The procedure includes dataset selection, data cleaning, preprocessing, stratified training and testing, model fitting, predictive evaluation, sustainability measurement, and comparative interpretation. The evaluation is supported by one workflow figure, tables describing predictive results and sustainability measurements, and comparative visualizations of classification and resource-efficiency outcomes. The results show that Extreme Gradient Boosting achieved the strongest overall classification performance, with an accuracy of 0.9994, macro recall of 0.9966, macro F1-score of 0.9956, macro precision of 0.9946, and one-versus-rest area under the curve of 0.9999, while requiring 0.002559 kilowatt-hours of energy and 11.83 seconds of execution time. Random Forest produced highly comparable predictive results with similarly low resource consumption. Histogram-based Gradient Boosting was the most efficient model in terms of time, energy use, and emissions, but its macro-level performance was substantially lower. Support Vector Machine produced acceptable predictive results but required substantially higher computational resources. These findings imply that sustainable intrusion detection should select models through a balanced evaluation of detection capability and computational cost rather than accuracy alone.
Co-Authors A Sumarudin A Sumarudin A. Sumarudin A. Sumarudin A. Sumarudin Ahmad Lubis Ghozali Arif Maulana Yusuf Arif Maulana Yusuf Borjulius, Renol Burjulius, Renol Darsih Darsih, Darsih Eko Marpanaji Hakim Asrori Ihsan Doni Irawan Indriyani, Widi Irawan, Joko Ismantohadi, Eka Iswanto, Muhammad Edi Khamdani, Luthfi Kurnia Adi Cahyanto Luthfi Khamdani Muhamad Zulfikar Azis Muhammad Anis Al Hilmi Muhammad Edi Iswanto Muhammaduthor, Fachrul Pralienka Bani Murinto Murinto Mustamiin, Muhamad Nurcahyon, Damar Qomarrudin, Muhammad Raswa Raswa Raswa, Raswa Renol Burjulius Robi Robiyanto Robianto, Robi Robiyanto, Robi Septafiansyah Dwi Putra Sri Handayaningsih Supardi Supardi