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All Journal CogITo Smart Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Handoko, Andrew C
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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PERBANDINGAN METODE SUPERVISED LEARNING UNTUK PREDIKSI DIABETES GESTASIONAL DENGAN SOFTWARE ORANGE Handoko, Andrew C; Hendry, Hendry
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 4 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i4.4166

Abstract

Dalam penelitian ini dilakukan analisa terhadap metode Supervised Learning dengan membandingkan hasil prediksi dari tiap metode, guna mendapatkan algoritma terbaik, yang dapat dikembangkan kedepannya sebagai salah satu media untuk mempermudah deteksi Diabetes Gestasional. Prediksi dilakukan terhadap Dataset Diabetes Gestasional yang di dapat dari Kaggle, dengan judul “Diabetes Dataset” yang berasal dari National Institute of Diabets and Digestive and Kidney Diseases. Dimana analisis akan menggunakan bantuan Software Orange, sebagai tempat untuk melakukan pengolahan data dan melihat nilai hasil prediksi dari masing-masing algoritma yang ada di metode Supervised Learning. Algoritma yang dibandingkan ada tujuh, dengan nilai Recall sebagai penentu no satu algoritma yang dianggap bagus untuk melakukan prediksi, diikuti dengan nilai Akurasi, Precisision, Test Time dan Train Time. Dan dengan bantuan Orange, maka di dapat algoritma yang paling bagus adalah Logistic Regression.
Optimizing Network Traffic Classification Models with a Hybrid Approach for Large-Scale Data Handoko, Andrew C; Hendry, Hendry; Wellem, Theophilus
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.966.281-294

Abstract

The escalating threat of cyberattacks necessitates the development of intrusion detection models that are both accurate and computationally efficient for large-scale network traffic. To address this issue, this study proposes a hybrid approach combining Autoencoder, Convolutional Neural Network (CNN), and XGBoost as an adaptive and lightweight solution for network traffic classification. The key contribution of this research lies in the design of a multi-stage pipeline that performs dimensionality reduction, feature extraction, and final classification. The model was evaluated using the Moore Dataset, which contains complex and high-dimensional network traffic data. The experimental results indicate that the proposed hybrid model achieved a classification accuracy of 99.20% with a testing time of only 0.09 seconds. Furthermore, the pipeline significantly reduced computational load compared to single CNN or XGBoost models. These findings demonstrate that the hybrid approach not only offers high classification performance but also enhances scalability and efficiency, making it suitable for real-world implementation in modern network security systems. Overall, the proposed model presents a promising and practical solution for advancing future intrusion detection systems.
Co-Authors Hendry Theopillus J. H. Wellem