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All Journal Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Iswanto
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.
Perbandingan Semantic Search Berbasis Sentence-Bert dan TF-IDF pada Pencarian Dokumen Ancaman Siber Nanda Setiawan; Johan; Iswanto; Roslina
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 2 (2026): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

The increasing number of cyber threat documents has made the information retrieval process increasingly complex because keyword-based methods are unable to understand semantic relationships between documents. This study aims to compare the performance of the Term Frequency–Inverse Document Frequency (TF-IDF) and Sentence Bidirectional Encoder Representations from Transformers (Sentence-BERT) methods in a cyber threat document retrieval system. The research stages include Importing the CyberBERT Dataset, text preprocessing, building a search model, testing using queries, and evaluating the results based on document relevance and response time. The Dataset used consists of 33 documents processed into vector representations using TF-IDF and 384-dimensional embedding using Sentence-BERT. The results show that both methods are able to return relevant documents to the given query, but produce different document sequences due to differences in text representation approaches. The TF-IDF method has a faster response time of 0.0089 seconds, while Sentence-BERT requires 0.0312 seconds. On the other hand, Sentence-BERT is better able to capture semantic relationships, resulting in more contextual searches. Thus, TF-IDF is superior in terms of efficiency, while Sentence-BERT is more effective in understanding the meaning of documents, making it suitable for semantic search systems in the cyber threat domain.
Co-Authors Farhan Sardy Abdillah Johan Nanda Setiawan Rahmat Humala Putra Hasibuan Ricky Irnanda Rika Rosnelly Romi Antoni Roslina, Roslina Susiana Khosasih Yiska Dayanti Zagoto