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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Building of Informatics, Technology and Science
nanda, afri
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Computer Science & IT

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Perbandingan Efektivitas Random Forest, SVM, dan Logistic Regression dalam Deteksi Intrusi Jaringan nanda, afri; wahyu, haditya; rahmaddeni, rahmaddeni; sutisna, sutisna; rinaldi, rinaldi
JATISI Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.10908

Abstract

Seiring dengan kemajuan teknologi, ancaman serangan siber semakin meningkat, sehingga risiko kebocoran data pun semakin besar. Dalam beberapa tahun terakhir, Indonesia kerap kali menghadapi serangan siber yang mengakibatkan hilangnya data-data penting, baik data perorangan maupun data lembaga pemerintahan. Kondisi ini menunjukkan bahwa diperlukannya solusi yang efektif untuk mendeteksi dan mencegah ancaman siber agar keamanan dan privasi data dapat terlindungi secara menyeluruh. Salah satu metode yang efektif untuk mendeteksi ancaman siber adalah machine learning. Penelitian ini bertujuan untuk mengevaluasi model machine learning dalam mendeteksi intrusi jaringan secara real-time. Pendekatan yang digunakan adalah teknik supervised learning dengan dataset yang mencakup trafik jaringan normal dan trafik yang mengandung serangan untuk melatih algoritmanya. Tiga algoritma yang diuji dalam penelitian ini adalah Support Vector Machine (SVM), Random Forest, dan Logistic Regression. Berdasarkan hasil pengujian ketiga model pendeteksian intrusi jaringan, pemodelan dengan hyperparameter tuning menunjukkan bahwa metode Random Forest memiliki akurasi tertinggi sebesar 95,87%, diikuti oleh Support Vector Machine sebesar 94,31%, dan Logistic Regression sebesar 88,72%. Sementara itu, tanpa penyetelan hiperparameter, Random Forest mencapai akurasi tertinggi sebesar 97,12%, diikuti oleh Support Vector Machine dengan 93,71% dan Logistic Regression dengan 89,88%.
Optimasi Deteksi Intrusi Jaringan Menggunakan Hybrid Model Autoencoder dan Random Forest Nanda, Afri; Nasution, Torkis
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9309

Abstract

Conventional Intrusion Detection Systems often suffer from performance degradation due to their inability to handle the complexity of high-dimensional data and class imbalance in modern network traffic. This study aims to optimize the Network Intrusion Detection System (IDS) by addressing the limitations of the Random Forest algorithm in handling high-dimensional data and its lack of model transparency (black-box). The proposed method is a Hybrid model integrating an Autoencoder as a non-linear feature extractor and Random Forest as a classifier. The Autoencoder is trained using a semi-supervised strategy to generate latent features and Reconstruction Error (MSE), which serves as a robust anomaly indicator. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance in the NSL-KDD dataset. To address the challenge of interpretability, SHAP-based Explainable AI (XAI) is strategically implemented to elucidate the complex interactions between the Autoencoder-compressed latent features and the final classification decisions, thereby transforming this hybrid architecture into a transparent system. Evaluation results demonstrate that the Hybrid Autoencoder-Random Forest model outperforms the Random Forest Baseline, achieving an Accuracy increase of 2.54% (to 77.61%) and a Recall increase of 3.96% (to 62.31%). The significant improvement in the Recall metric empirically validates the effectiveness of hybrid features, specifically the Reconstruction Error, in detecting Zero-Day attacks characterized by unknown patterns. Furthermore, SHAP visualization successfully reveals the contribution of latent features, providing crucial transparency for network security forensic analysis.
Co-Authors Rahmaddeni Rahmaddeni Rinaldi Rinaldi Sutisna Sutisna Torkis Nasution wahyu, haditya