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All Journal Sinergi Brilliance: Research of Artificial Intelligence
Al-Faris, Muhammad Ghozi
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Optimizing intrusion detection with data balancing and feature selection techniques Elsi, Zulhipni Reno Saputra; Supli, Ahmad Affandi; Jimmie, Jimmie; Al-Faris, Muhammad Ghozi; Rapel, David Agustianto
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.019

Abstract

The rapid growth of IoT devices has brought significant security challenges, particularly in detecting various types of attacks within heterogeneous network environments. This study explores the effectiveness of data balancing techniques, including Random Undersampling (RUS), Cost-Sensitive Learning (CSL), Synthetic Minority Oversampling Technique (SMOTE), and Randomized Combination Sampling (RCS). Feature selection methods, namely correlation (threshold 0.8) and mutual information (top 15 features), were employed to optimize feature sets. The Decision Tree (DT) and Linear Discriminant Analysis (LDA) classifiers were used to evaluate the performance of balanced datasets. The evaluation metrics included accuracy, precision, recall, F1-score, G-mean, and ROC curves. The results revealed that SMOTE and RCS outperformed other balancing methods, with SMOTE achieving the highest accuracy (98.7%) and RCS demonstrating robust G-mean values across both feature selection techniques. DT consistently showed better performance compared to LDA across all metrics, while feature selection significantly improved the classification results, particularly under mutual information criteria. However, the analysis highlighted limitations of LDA in handling imbalanced datasets and high-dimensional features. This study concludes that a combination of advanced data balancing and effective feature selection significantly enhances the accuracy of intrusion detection in IoT networks. Future work will focus on integrating real-time detection systems and exploring hybrid models to further improve the detection of complex attacks in dynamic IoT environments. 
IoT-Based Electrical Power Consumption Monitoring System in Households Using ESP32 and PZEM-004T Hidayat, Kemas Muhammad Wahyu; Al-Faris, Muhammad Ghozi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6368

Abstract

Electricity consumption in the household sector is often poorly controlled, leading to energy waste and increased electricity bills. To address this issue, this study presents the design and implementation of an electricity consumption monitoring system based on the Internet of Things (IoT) using the ESP32 microcontroller and PZEM-004T sensor. The system allows real-time monitoring of electrical parameters such as voltage, current, power, and energy, with data displayed on digital devices like smartphones or computers.  Measurement data from the sensor is transmitted wirelessly to an IoT platform via Wi-Fi, enabling users to monitor electricity usage anytime and anywhere. A prototype method was used in this research, covering hardware and software design, sensor integration, and system testing.The testing results show that the system effectively provides accurate and responsive electricity usage data. With this system, users can identify usage patterns, detect high-power appliances, and make informed decisions to improve energy efficiency. The ability to access real-time data helps prevent energy waste and control monthly costs more effectively.Beyond individual benefits, this system also supports wider energy conservation initiatives by promoting conscious energy consumption behavior. The integration of IoT technology in household energy management demonstrates a practical solution for creating smarter and more sustainable living environments. This study confirms the potential of IoT-based systems to enhance energy awareness and support efforts in reducing overall electricity consumption.
Co-Authors Hidayat, kemas Muhammad Wahyu Jimmie, Jimmie Rapel, David Agustianto Supli, Ahmad Affandi Zulhipni Reno Saputra Els