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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) JURNAL ILMIAH INFOKOM
Romi Antoni
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering 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.
EXPLAINABLE MACHINE LEARNING UNTUK PREDIKSI HARGA MOBIL BEKAS DAN ANALISIS FAKTOR PENENTU HARGA Dwi Robiul R; M. Al-Adib; Romi Antoni; Diyo Mollana F; Rahmad S; Fauzan Hamdi R; Adil Setiawan
INFOKOM (Informatika & Komputer) Vol 13 No 1 (2025): JURNAL INFOKOM JUNI 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i1.2322

Abstract

This research aims to predict used car prices and analyze the price determinants using an Explainable Machine Learning (XAI) approach. Used car price prediction presents a significant challenge in the automotive market, where pricing is influenced by various complex variables. The methodology involves comparing the performance of two machine learning models: linear regression (LR) and random forest (RF), trained on a dataset comprising 2,059 used car data points and 19 engineered features. The best-performing model is then interpreted using the SHAP (SHapley Additive exPlanations) method to identify the contribution of each feature. The evaluation results demonstrate that the Random Forest (RF) model exhibits superior performance compared to the Linear Regression model. The Random Forest model achieved a coefficient of determination (R2) of 0.819 and a Mean Absolute Error (MAE) of 294,591.0 . This performance is significantly better than the linear regression model, which yielded an R2 of 0.771 and an MAE of 716,221.3. The SHAP interpretive analysis identified the most significant price determinants. In sequential order, the five most dominant factors influencing price prediction are max power, car age, vehicle length (length_num), vehicle width (width_num), and kilometer (mileage). This finding provides transparent and justifiable insights into the key variables underlying price fluctuations in the used car market.
Co-Authors Diyo Mollana F Dwi Robiul R Farhan Sardy Abdillah Fauzan Hamdi R Iswanto M. Al-Adib Rahmad S Rahmat Humala Putra Hasibuan Ricky Irnanda Rika Rosnelly Setiawan, Adil Susiana Khosasih Yiska Dayanti Zagoto