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Torkis Nasution
Program Studi Teknik Informatika, STMIK Amik Riau
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Journal : Building of Informatics, Technology and Science

 1 
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 -, Dwi Haryono -, Tashid Adyanata Lubis Afnand Fachzevi Ambiyar, Ambiyar Anam, M Khairul Andesa, Khusaeri Andi Cahyono Arden Simeru Aurelia, Zahra Nur Dedy Irfan Dwi Haryono Efrizoni, Lusiana Elyandri Prasiwiningrum Erlinda, Susi Esi Tri Emerlada Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Ganefri Ganefri Gunadi Gunadi Gusrio Tendra Hadi Asnal, Hadi Haryadi, Octadino Helda Yenni, Helda Herwin Herwin -, Herwin Herwin Herwin Herwin Herwin, Herwin Imardi, Syahrul Johan Johan Julianto Royand Wahyudi simanullang Khusaeri Andesa Khusaeri Andesa Khusaeri Andesa Kudadiri, Parlindungan Mardainis Mardainis Mardhiah Masril Muhammad Dzaki Salman Muhammad Haryandi Muhammad Patra Hafizh nanda, afri Nurjayadi Nurjayadi Nurjayadi Nurjayadi Octadino Haryadi Oskah Dakhi Rahmaddeni Rahmaddeni Rahmiati Rahmiati -, Rahmiati Rangga Rahmadian Yuliendi Ridwan Ridwan Rini Yanti Rini Yanti Rohmat Romadhoni Rometdo Muzawi, Rometdo Shandy Merdya Pratama Shandy Merdya Pratama Simeru, Arden Soni Supahri, Hafid Azis SUSANTI Susanti, Susanti SUSI ERLINDA Susi Erlinda Syahrul Imardi T Sy Eiva Fatdha T Sy Eiva Fatdha, T Sy Eiva Triyani Arita Fitri Unang Rio Unung Verawardina, Unung Wakhinuddin Wakhinuddin Widi, Rahma Wilda Susanti Wirta Agustin Yandri Armi Yanti, Rini Yoyon Efendi Yuliendi, Rangga Ramadian