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All Journal Jurnal Informatika Kaputama (JIK) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Bayu Tri Nugroho, Bayu Tri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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DETEKSI INTRUSI JARINGAN MENGGUNAKAN ARSITEKTUR HYBRID CNN-LSTM MELALUI PENYELARASAN FITUR LINTAS DATASET TON-IOT DAN CIC-IDS Nugroho, Bayu Tri; Erik IH Ujianto; Rianto; Kuswijayanto, Adi Cahyo
Jurnal Informatika Kaputama (JIK) Vol 10 No 2 (2026): Volume 10, Nomor 2, Juli 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v10i2.1323

Abstract

Meningkatnya kompleksitas lalu lintas jaringan dan heterogenitas lingkungan Internet of Things (IoT) menimbulkan tantangan serius bagi sistem deteksi intrusi (Intrusion Detection System/IDS) konvensional, khususnya dalam mendeteksi serangan baru dan lintas-domain. Meskipun pendekatan deep learning telah menunjukkan performa tinggi, sebagian besar penelitian masih terbatas pada satu dataset sehingga kemampuan generalisasi model menjadi rendah. Penelitian ini mengusulkan pendekatan hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) yang dilatih pada dataset lintas-domain melalui proses penyelarasan fitur antara CSE-CIC-IDS2018 dan ToN-IoT. Pra-pemrosesan meliputi normalisasi satuan waktu, rekayasa fitur turunan, penanganan missing value, encoding atribut, serta Min–Max scaling untuk menghasilkan dataset yang homogen. Kinerja model CNN, LSTM, dan CNN–LSTM dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score dengan variasi learning rate 0.01, 0.001, dan 0.0001. Hasil eksperimen menunjukkan bahwa model CNN–LSTM dengan learning rate 0.001 memberikan performa terbaik dengan akurasi 99,3%, recall 99,8%, dan F1-score 99,4%, serta stabilitas pelatihan yang lebih baik dibandingkan model tunggal. Temuan ini menunjukkan bahwa integrasi dataset heterogen dan arsitektur hybrid CNN–LSTM mampu meningkatkan efektivitas dan generalisasi IDS pada lingkungan jaringan yang beragam.
Perbandingan Model Deep Learning LSTM, GRU, dan Bi-LSTM untuk Prediksi Hujan Harian Australia Menggunakan Teknik SMOTE Risnanto, Ari; Nugroho, Bayu Tri; Hermawan, Arief; Avianto, Donny
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11691

Abstract

Climate change causing increasingly erratic rainfall patterns, triggering an increase in hydrometeorological disasters such as floods, droughts, and declining agricultural productivity. Therefore, accurate rainfall prediction is crucial for mitigation and decision-making. However, previous research often focuses solely on accuracy metrics without evaluating the model's computational burden, and often ignores the problem of class imbalance in weather datasets. This study evaluates the performance and computational efficiency of LSTM, GRU, and Bi-LSTM deep learning models for daily rainfall prediction using the historical Australian meteorological dataset weatherAus. The novelty of this study lies in the comprehensive mapping between predictive quality and resource efficiency after dataset balancing. The preprocessing stage includes handling missing values, categorical data transformation, data leakage prevention, data sharing, and the application of SMOTE oversampling. The results of the area under the curve (AUC-ROC) evaluation show that the GRU model is superior with a value of 0.85, surpassing LSTM and Bi-LSTM, respectively, at 0.84. In the rain class recall metric, GRU again leads (0.70), compared to LSTM (0.67), and Bi-LSTM (0.57). Computational evaluation, GRU is significantly more efficient with the fastest training time (1,306.26 seconds), followed by LSTM (2,259.12 seconds), and Bi-LSTM (13,348.43 seconds). Peak RAM usage relatively comparable, GRU (2,053.77 MB), LSTM (1,971.47 MB), and the highest Bi-LSTM (2,242.60 MB). These findings conclude that GRU is recommended as the most optimal model that balances accuracy and efficiency, LSTM as an alternative, while Bi-LSTM is considered less effective. Future research recommended to explore hybrid architectures or ensemble learning to capture more complex spatiotemporal patterns.
Co-Authors Donny Avianto Erik IH Ujianto Handriyono Handriyono Kuswijayanto, Adi Cahyo Rianto Risnanto, Ari