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All Journal International Journal of Artificial Intelligence Research UPGRADE: Jurnal Pendidikan Teknologi Informasi
Dzul Fadli Rahman
Universitas Negeri Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Other

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Machine Learning-Based Network Traffic Anomaly Detection Using the CIC-IDS2017 Dataset Nadhir Fachrul Rozam; Tika Novita Sari; Muhammad Resa Arif Yudianto; Dzul Fadli Rahman
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.
Prototype of IoT-Enabled RFID Door Lock System Using n8n Workflow Integration Ilmawan Mustaqim; Dzul Fadli Rahman; Heri Nurdiyanto; Abdul Khaliq Pangestu; Fauzi Wijaya
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1733

Abstract

This study aims to develop a Radio Frequency Identification (RFID)-based door lock system integrated with the n8n automation platform as an innovative solution to enhance access security within an academic envi-ronment. The research employs a Research and Development (R&D) method with a Waterfall Model approach, consisting of requirement analysis, system design, hardware and software implementation, and prototype test-ing stages. The system utilizes an RFID RC522 module, an ESP8266 microcontroller, an I2C LCD, and a 12V Li-Ion battery, integrated through an n8n workflow to automatically record access data into an Excel data-base. The test results show that the system can perform user authentication and real-time access logging with 100% accuracy and an average response time of five seconds. The casing, printed using a 3D printer with PLA filament, provides a lightweight, durable, and easily assembled design. Overall, the developed system functions effectively as both a control and monitoring tool, demonstrating potential as a model for smart security system development in higher education institutions
Co-Authors Abdul Khaliq Pangestu Fauzi Wijaya Heri Nurdiyanto Muhammad Resa Arif Yudianto Mustaqim, Ilmawan Nadhir Fachrul Rozam Tika Novita Sari