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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Global Engineering Research & Science (J-GERS)
Abd Hamid, Nazirah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science

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Determination of better insulating oil for power transformer oil application using Analytical Hierarchy Process (AHP) Arief, Yanuar Z.; Abd Hamid, Nazirah; Izzwan Saad, Mohd Hafiez; Al Hakim, Rosyid R
Journal of Global Engineering Research and Science Vol. 1 No. 1 (2022): Journal of Global Engineering Research & Science
Publisher : Jakarta Global University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56904/jgers.v1i1.17

Abstract

The Analytical Hierarchy Process (AHP) which was developed by Saaty is a decision-making tool in the year of 1970s. The system is able to solve the complex analysis which could not be performed by the human brain. In this project, the AHP system is chosen to select a better insulating oil for power transformer oil application since the insulation is the most important part of a transformer. In this research method based on the AHP technique, there are six criteria are involved in the model, namely breakdown voltage, dissipation factor/tan delta, pour point, kinematic viscosity, density, and flash point. While, the given alternatives are palm oil, canola oil, palm fatty acid ester (PFAE) oil, corn oil, sunflower oil, and soybean oil, respectively. A spreadsheet software application (Microsoft Excel) was employed in this study. As the result, it is found that the PFAE oil is the best insulating oil that able to be used in a real power transformer oil application. All requirements as insulating oil for transformer oil of PFAE oil are met significantly.
Botnet detection: a system for identifying DGA-based botnets using LightGBM Mohamad, Mumtazimah; Abd Hamid, Nazirah; A. Ghaleb, Sanaa A.; Mohd Satar, Siti Dhalila; Safei, Suhailan; Fazamin Wan Hamzah, Wan Mohd Amir; En En, Lim
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp833-844

Abstract

Botnets present a major challenge to detecting anomalies in domain generation algorithms (DGAs). Botmasters use DGAs to create numerous domain names to communicate with command-and-control servers, complicating the detection process. Traditional blacklisting methods struggle to effectively identify anomalous DGA domain names amid the vast number of randomly generated domains, leading to a greater risk of detection being evaded. The proliferation of DGA-based botnets has created an urgent need for robust detection methods. Various techniques and attributes have been utilised to categorise different DGA families, yet the dynamic nature of DGA domain names renders the current blacklisting algorithms ineffective. Additionally, the dynamic characteristics of DGAs further complicate classification, emphasising the need for machine learning models to improve detection accuracy and enhance cyber defence. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection.
Co-Authors A. Ghaleb, Sanaa A. Al Hakim, Rosyid R Arief, Yanuar Z. En En, Lim Fazamin Wan Hamzah, Wan Mohd Amir Izzwan Saad, Mohd Hafiez Mohamad, Mumtazimah Mohd Satar, Siti Dhalila Safei, Suhailan