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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Susiana Khosasih
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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A Analisis Perbandingan CNN, SVM, dan Hybrid CNN-SVM untuk Deteksi Anomali Trafik Jaringan Susiana Khosasih; Romi Antoni; Ricky Irnanda; Iswanto; Rahmat Humala Putra Hasibuan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.748

Abstract

The rapid growth of information technology has significantly increased the volume and complexity of network traffic, leading to cyber security threats that are increasingly dynamic and difficult to detect using traditional security systems. The limitations of signature-based detection systems in identifying new attacks, including zero-day attacks, necessitate the adoption of more adaptive anomaly detection approaches through the utilization of machine learning and deep learning within Network Intrusion Detection Systems (NIDS). This study aims to analyze and compare the performance of Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and a hybrid CNN–SVM model in detecting network traffic anomalies. This research employs a quantitative approach using an experimental method to evaluate the performance of the three models based on the CIC-IDS2017 dataset. The experimental process includes data preprocessing, model development, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the CNN and SVM baseline models achieve high accuracy levels of 98.85% and 98.66%, respectively, but still exhibit limitations in detecting minority attack classes. The hybrid CNN–SVM model achieves the best performance with an accuracy of 99.41% and a more balanced macro-average recall, indicating improved generalization across classes. The integration of CNN as a feature extractor and SVM as a classifier is proven to be effective in leveraging the complexity of network traffic features while enhancing classification stability. Therefore, the hybrid CNN–SVM approach can be recommended as a more effective and reliable network traffic anomaly detection method compared to single-model approaches in supporting modern network security systems.
Klasifikasi Penyakit Daun Tomat Menggunakan Pengolahan Citra Dan Algoritma Machine Learning Romi Antoni; Susiana Khosasih; Ricky Irnanda; Iswanto; Farhan Sardy Abdillah; Yiska Dayanti Zagoto; Rika Rosnelly
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.778

Abstract

Klasifikasi penyakit daun tomat merupakan langkah penting untuk meningkatkan produktivitas pertanian dan meminimalkan kerugian akibat patogen. Penelitian ini bertujuan membandingkan dan mengevaluasi performa algoritma Naive Bayes dan Support Vector Machine (SVM) dalam klasifikasi penyakit daun tomat berbasis pengolahan citra digital. Pipeline penelitian mencakup segmentasi citra berbasis HSV, ekstraksi fitur warna, bentuk, dan tekstur menggunakan metode Gray Level Co-occurrence Matrix (GLCM) dan Local Binary Pattern (LBP), serta proses klasifikasi. Sistem diimplementasikan dalam bentuk Graphical User Interface (GUI) berbasis MATLAB untuk memudahkan manajemen data latih, pelatihan model, klasifikasi, dan evaluasi performa. Hasil pengujian menunjukkan bahwa SVM mencapai akurasi 92,36%, lebih tinggi dibandingkan Naive Bayes sebesar 79,41%. Kontribusi penelitian ini meliputi analisis komparatif Naive Bayes dan SVM dalam klasifikasi penyakit daun tomat, integrasi fitur warna, bentuk, dan tekstur dalam satu pipeline, dan pengembangan GUI interaktif untuk klasifikasi. Penelitian ini diharapkan dapat mendukung pertanian presisi melalui deteksi penyakit daun tomat yang lebih cepat, akurat, dan efisien.
Co-Authors Andri Armaginda Siregar Farhan Sardy Abdillah Iswanto Jalaluddin Nasution Mhd. Agung Irnanda Rahmat Humala Putra Hasibuan Ricky Irnanda Rika Rosnelly Romi Antoni Wahyu Saptha Negoro Yiska Dayanti Zagoto