Implementasi algoritma gradient boosting machine untuk deteksi intrusi pada jaringan komputer
DOI:
https://doi.org/10.24246/itexplore.v5i2.2026.pp119-130Keywords:
Gradient Boosting Machine, Machine Learning, Deteksi Intrusi, JaringanAbstract
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.
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A. Khaliq and S. Novida Sari, “Pemanfaatan Kerangka Kerja Investigasi Forensik Jaringan Untuk Identifikasi Serangan Jaringan Menggunakan Sistem Deteksi Intrusi (Ids),” Jurnal Nasional Teknologi Komputer, vol. 2, no. 3, pp. 150–158, 2022, doi: 10.61306/jnastek.v2i3.52.
R. Ahmad, “Survei Teknik Pemilihan Fitur Untuk Sistem Deteksi Intrusi Berbasis Machine Learning,” Infotek: Jurnal Informatika dan Teknologi, vol. 8, no. 1, pp. 317–323, 2025, doi: 10.29408/jit.v8i1.28657.
H. F. Fajar, A. Fadhila, and M. K. Yades, “Redesain Kebijakan Hukum Keamanan dan Ketahanan Siber: Studi Kasus Serangan Siber pada Pusat Data Nasional Tahun 2024,” Rechts Vinding: Media Pembinaan Hukum Nasional, vol. 14, no. 2, 2025. doi: 10.33331/rechtsvinding.v14i2.2155.
C. L. Mindara, A. Zulianto, H. P. Utomo, T. Hatati, and G. P. Mindara, “Deteksi Intrusi Untuk Klasifikasi Serangan Jaringan Dengan Penerapan Algoritma Convolutional Neural Network,” Jurnal ICT : Information Communication & Technology, vol. 23, pp. 517–522, 2023.
R. P. Simanjuntak and R. R. M. Sijabat, “Meningkatkan Keamanan Siber dalam Lingkungan Internet of Things (IoT) dengan Menggunakan Sistem Deteksi Intrusi Berbasis Pembelajaran Mesin,” DIKE : Jurnal Ilmu Multidisiplin, vol. 2, no. 2, pp. 62–68, 2024, doi: 10.69688/dike.v2i2.106.
B. Ramadhan, D. Firdaus, and A. R. Rafi, “MIND (Multimedia Artificial Intelligent Networking Database Teknik SMOTE Sebagai Solusi Imbalance Class dalam Model Deteksi Intrusi DDoS dengan Metode PCA-Random Forest,” Journal MIND Journal | ISSN, vol. 8, no. 1, pp. 52–64, 2023, doi: 10.26760/mindjournal.v8i1.52-64.
F. Wibowo, “Analisis Algoritma Kecerdasan Buatan pada Pengembangan Sistem Deteksi Intrusi pada Jaringan Komputer,” Duniailmu.org, vol. 3, no.9, 2023.
A. Sulandri, A. Basuki, and F. A. Bachtiar, “Metode Deteksi Intrusi Menggunakan Algoritma Extreme Learning Machine dengan Correlation-Based Feature Selection,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 1.
D. P. Sari, Z. Halim, I. Irlon, B. Waseso, and S. Saromah, “Implementasi Machine Learning untuk Deteksi Intrusi pada Jaringan Komputer,” Jurnal Minfo Polgan, vol. 13, no. 2, pp. 1389–1394, 2024, doi: 10.33395/jmp.v13i2.14074.
N. F. Wardhani, “Penggunaan Machine Learning dalam Deteksi Intrusi pada Jaringan Komputer,” Duniadata, vol. 1, no. 4, 2024.
J. Dewanto, “Analisis Perbandingan Dataset KDD’99, UNSW-Nb15, dan CICIDS2017 untuk Sistem Deteksi Intrusi,” Kohesi: Jurnal Multidisiplin Saintek, vol. 2, no. 9, 2024.
R. Rianti and R. Andarsyah, “Memprediksi Tingkat Atrisi Karyawan Menggunakan Machine Learning,” Jurnal Tekno Insentif, vol. 18, no. 1, pp. 39–52, 2024, doi: 10.36787/jti.v18i1.1263.
M. I. Rizaldi, D. R. Chandranegara, and D. R. Akbi, “Comparison of Machine Learning Techniques for Classification of Distributed Denial of Service Attacks based on Feature Engineering in SDN-Based Networks,” JIPI Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, vol. 9, no. 3, 2024.
I. Wardhana, M. Ariawijaya, and V. A. Isnaini, “Gradient Boosting Machine, Random Forest dan Light GBM untuk Klasifikasi Kacang Kering,” JURNAL RESTI, vol. 6, no. 1, 2022.
A. Oyelakin, “Overview and Exploratory Analyses of CICIDS 2017 Intrusion Detection Dataset,” J Syst Eng Inf Technol, vol. 2, no. 2, pp. 45–52, 2023.
M. Catillo, A. Del Vecchio, A. Pecchia, and U. Villano, “A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection,” ACM International Conference Proceeding Series, 2023, doi: 10.1145/3600160.3605031.
P. A. Khairunnisa, N. Annisa, and J. Parhusip, “Perancangan Sistem Keamanan Jaringan Berbasis Cybersecurity untuk Mitigasi Ancaman Siber pada Infrastruktur TI: Studi Kasus di Indonesia,” Jurnal Ilmu Teknik dan Informatika, vol. 4, pp. 9–16, 2024.
F. Septian, “Optimasi Klusterisasi pada Lama Tempo Pekerjaan Berbasis Gradient Boost Algorithm,” Indonesian Journal of Information Technology, vol. 20, no. 20, 2023.
A. R. Arrahimi, M. K. Ihsan, D. Kartini, et al., “Teknik Bagging dan Boosting pada Algoritma CART untuk Klasifikasi Masa Studi Mahasiswa,” Jurnal Sains dan Informatika, vol. 5, no. 1, Jun. 2019.
N. H. Setyawan and N. Wakhidah, “Analisis Perbandingan Metode Logistic Regression, Random Forest, Gradient Boosting untuk Prediksi Diabetes,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 1, Mar. 2025, doi: https://doi.org/10.29100/jipi.v10i1.5743.
L. W. Rizkallah, "Enhancing the performance of gradient boosting trees on regression problems," Journal of Big Data, vol. 12, no. 35, 2025, doi: https://doi.org/10.1186/s40537-025-01071-3.
B. Ramadhan, D. Firdaus, and A. R. Rafi, “Teknik SMOTE Sebagai Solusi Imbalance Class dalam Model Deteksi Intrusi DDoS dengan Metode PCA-Random Forest,” Journal MIND Journal | ISSN, vol. 8, no. 1, pp. 52–64, 2023.
R. Mubarok, A. A. Syahputra, A. T. Permana, L. Sholiah, and Tarwoto, “Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 9, no. 2, 2025.
R. R. Aria, “Implementasi Algoritma K-Means untuk Pengelompokan Data Imunisasi Balita dengan Metode CRISP-DM,” Remik, vol. 9, no. 1, pp. 189–197, 2025, doi: 10.33395/remik.v9i1.14391
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