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All Journal Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Pelita - Jurnal Penelitian Mahasiswa UNY JIKO (Jurnal Informatika dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Farmasi Indonesia Jurnal Tadbir Peradaban Jurnal Atma Inovasia KONTAN: Jurnal Ekonomi, Manajemen dan Bisnis UPGRADE: Jurnal Pendidikan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Tika Novita Sari
Unknown Affiliation
Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Medicine & Pharmacology Social Sciences Other

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Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen dengan Bayesian Optimization Yudianto, Muhammad Resa Arif; Zakariah, Masduki; Rozam, Nadhir Fachrul; Rahman, Dzul Fadli; Sari, Tika Novita; Mustofa, Zaenal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11524

Abstract

This study examines the effect of Bayesian Optimization in improving the performance, computational efficiency, and sustainability of Aspect-Based Sentiment Analysis models using Support Vector Machine (SVM). A dataset consisting of 988 customer reviews about Borobudur Temple, classified into six dimensions: Attractiveness, Facilities, Accessibility, Visual Image, Price, and Human Resources is used to compare two scenarios, namely Baseline SVM and SVM enhanced with Bayesian Optimization (BO). Important metrics used include accuracy, computational duration, energy usage, and carbon emissions. The results show that BO significantly improves accuracy, especially on difficult aspects such as Facilities (from 0.7294 to 0.8682) and Price (from 0.8047 to 0.9576). The most complicated aspect, namely visual image due to the very minimal number of datasets (unbalanced), achieved an increase in accuracy from 0.6729 to 0.72. In addition, BO reduces training time, especially for resource-intensive tasks such as the visual image aspect, reducing training time from 13.04 seconds to 9.4 seconds. Substantial reductions in energy consumption and CO₂ emissions are seen in line with sustainable machine learning principles. The hyperparameter adaptability of SVM, with linear kernels performing well in simpler tasks, while polynomial and sigmoid kernels improve performance for more complex parts. BO substantially alleviates the limitations of Baseline SVM, offering a robust, efficient, and environmentally friendly solution for ABSA. Future research can explore more enhancements for complex tasks to improve performance and efficiency.
Machine Learning-Based Network Traffic Anomaly Detection Using the CIC-IDS2017 Dataset Rozam, Nadhir Fachrul; Sari, Tika Novita; Yudianto, Muhammad Resa Arif; Rahman, Dzul Fadli
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6174

Abstract

The increasing volume and diversity of traffic in modern networks demand more adaptive intrusion detection approaches than traditional signature-based methods. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting multi-class network traffic anomalies using the  CIC-IDS2017 dataset. The research process includes data cleaning and transformation,  class imbalance handling through random undersampling, and the implementation of five classification models: Logistic Regression, Gaussian NaïveBayes, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Model performance is assessed using accuracy, precision, recall, and F1-score, supported by confusion matrix analysis and feature contribution evaluation. The results indicate that Random Forest achieves the best performance with an accuracy of 99.44% and consistently high evaluation metrics, while Gaussian Naïve Bayes shows the lowest performance. Furthermore, flow-based features are found to play a dominant role in improving classification accuracy, while misclassifications mainly occur among classes with similar traffic patterns. The findings highlight that selecting appropriate algorithms and applying effective preprocessing strategies are critical for developing more accurate and adaptive intrusion detection systems capable of addressing evolving cyber threats.
Co-Authors Andiny, Intan Suci Arih Afra Inayah Darmeinis, Darmeinis Dewi, Lucia Vita Inandha Dewita, Viora Rahma Dyah Susilowati Dzul Fadli Rahman Isjoni Isjoni Liza, Asma Masduki Zakarijah Muhammad Resa Arif Yudianto Nadhir Fachrul Rozam Nafngan Fitriansah Putra, Ramdani Bayu Rahmadina, Salsabilla Rahman, Dzul Fadli Ria Astriratma Sri Mulyana Tesmanto, Joni Tri Hardiyanti Tugiman ' Zaenal Mustofa Zakariah, Masduki