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All Journal IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Dileando Gamaliel
Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana
Computer Science & IT Control & Systems Engineering

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Implementasi algoritma gradient boosting machine untuk deteksi intrusi pada jaringan komputer Dileando Gamaliel; Wiwin Sulistyo
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp119-130

Abstract

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.
Co-Authors Wiwin Sulistyo