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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 3,126 Documents
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Performance evaluation of enhanced pulse width modulation techniques for cascaded multilevel inverter Jayaprakasam Vinothini; Ramkumar Ravindran
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10718

Abstract

The nine-level multi-level inverter (MLI) uses a DC voltage supply, seven switches, four capacitors, and twelve diodes to generate an AC output with nine distinct voltage levels. Control strategies like pulse width modulation (PWM), fuzzy logic control (FLC), and time ratio control (TRC) regulate the inverter’s performance. Multi-carrier PWM techniques such as APOD, POD, PD, and multi-reference PWM are applied for precise control. The system is simulated in MATLAB, and performance is evaluated based on output voltage RMS and total harmonic distortion (THD), ensuring compliance with IEEE standards. FLC and TRC have been proposed as control approaches for the mighty nine-level MLI. The proposed inverter uses 60% fewer components than traditional designs while giving better power quality. All methods performed better than existing field-programmable gate array (FPGA)-based systems that had 19.86% THD. The performance evaluation includes output voltage rms and THD indices, requirements that must meet IEEE standards. The proposed strategies are compared with existing techniques in the literature.
Design and evaluation of a Python-based network automation system for internet of things devices Eslam Samy El-Mokadem; Bilal Bataineh; Samy El-Mokadem; Abdelmoty M. Ahmed; Mohamed A. Torad
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9562

Abstract

The increasing demand for the internet of things (IoT) and massive machine-type communications has significantly expanded network size and complexity. Recent research indicates that 95% of network tasks are monitored manually, leading to configuration complexity, human errors, faults, downtime risks, and time consumption. Network automation emerges as a practical solution by reducing administrative overhead and enabling reliable, scalable, and self-managing networks through scripting and standardized programming languages. This paper proposes a model for automated networks using Python-based methods, specifically Paramiko, Netmiko, and the network automation and programmability abstraction layer with multivendor support (NAPALM), to configure the enhanced interior gateway routing protocol (EIGRP) within the graphical network simulator-3(GNS3) environment. The performance of the automated network was evaluated using two scenarios: with threading and without threading. Key metrics included execution time, configuration accuracy, error rates, and resource utilization. Simulation results demonstrate that the automated approach significantly outperforms manual configuration. In addition, the automated model with threading outperformed the automated model without threading, achieving execution time reductions up to 67% and 100% configuration accuracy with zero errors. These findings underscore the effectiveness of the proposed system for automating complex network tasks in large-scale IoT deployments.
Implementation of meta-heuristic and deep learning algorithms for power system cybersecurity Baddu Naik Bhukya; Samanthaka Mani Kuchibhatla; Naresh Kumar Bhagavatham; Tirumalasetti Lakshmi Narayana; Madhava Rao Chunduru; Balakrishnan Koustubha Madhavi
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8569

Abstract

Power system cyber security is crucial due to their criticality. Cybersecurity is essential to protect vital infrastructure as power systems digitize. Meta-heuristic and deep learning techniques are used to improve power system cyber security in this paper. To evaluate their performance, the suggested approach is compared to traditional supervised machine learning algorithms including artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The technique optimizes deep learning model hyper parameters and architectures to detect cyber risks. Cyberattacks on power systems can cause service outages and cascading failures with extensive social implications. Meta-heuristic and deep learning algorithms are integrated to improve power system cyber security in this study. Deep learning is good at pattern recognition and anomaly detection, while meta-heuristic algorithms optimize efficiently. A complete threat detection and mitigation strategy is proposed by merging these methodologies. The proposed methodology tests classic supervised machine learning algorithms such ANNs, CNNs, and SVMs. Simulations showed the algorithm worked better. It beat competition in accuracy, precision, recall, and F1-score.
Evaluation of the possibility of using an antenna switch with a wideband matching device Zhanat Manbetova; Assel Yerzhan; Zharkyn Altynov; Zukhra Rakhimzhanova; Gulzada Mussapirova; Pavel Boykachev; Assanali Iskakov; Maria Poleshchuk
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10439

Abstract

This paper investigates the feasibility of integrating a broadband matching device (BMD) with a high-frequency switch into the antenna system of the MIC RL-400M radio relay station, part of the "ROSA" radar complex. The aim is to compensate for antenna impedance changes caused by adverse weather conditions such as snowfall, icing, and wet snow, which reduce power transmission efficiency. Various types of high-frequency switches, including relays, PIN diodes, and transistors, were analyzed. A transistor-based switch (HMC349AMS8G) was selected due to its low insertion loss, high reliability, and wide operating frequency range. The BMD structure was synthesized to minimize impedance variation, and its performance was evaluated through simulation in AWR Microwave Office and experimental measurements in the 394–450 MHz range. Results showed an average power loss reduction of 0.7 dB and a 6.9% increase in radio link range compared to operation without a switch. The proposed solution enhances the stability and efficiency of radar and radio relay systems, ensuring reliable operation in challenging environmental conditions.
Security challenges in the internet of things for higher education: a study of vulnerabilities and emerging solutions Kamal Elhattab; Driss Naji; Abdelouahed Ait Ider; Abdelali Joumad; Abdelkbir Ouisaadane; Karim Abouelmehdi
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10464

Abstract

The growing use of internet of things (IoT) technologies in higher education is transforming how institutions manage infrastructure, deliver teaching, and engage with students. While these advancements offer considerable benefits, they also introduce significant security risks. Common threats include weak access controls, insufficient data protection, outdated software, exposure to denial-of-service (DoS) attacks, and lack of physical safeguards for connected devices. This study provides a comprehensive review of these vulnerabilities within academic environments and proposes a security framework adapted to the specific operational and technical realities of universities. Unlike generic approaches, this research focuses on the unique challenges of higher education, such as decentralized information technology (IT) structures, limited resources, and diverse user groups. The main contribution lies in identifying and evaluating security measures that are both effective and applicable in academic contexts. These include encryption methods, identity verification techniques, secure update mechanisms,and intelligent systems for detecting abnormal behavior. The analysis is supported by case examples from real institutions, illustrating both successes and limitations of current practices. This work aims to guide educational institutions in improving the resilience of their IoT systems. It also outlines areas for future research, particularly in the development of lightweight and scalable security solutions suited to the evolving needs of smart learning environments.
Data mining approach for stunting clusters in Jumput Rejo Amir Ali; Purwanto Purwanto; Mundakir Mundakir
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8035

Abstract

The target of reducing the stunting prevalence rate by 14% in 2024 which has been set by the government needs to be of concern to be implemented by the local health office. The purpose of the research is to cluster toddler anthropometry data with data mining algorithm. Optimize K-Means (KM) algorithm with elbow method use to cluster toddler anthropometry data (sex, height, weight, age, and health care center). A set of 580 children's anthropometric measurements were analyzed and categorized based on their similarity. Cluster 1 comprises 150 members and exhibits a narrower range of age and height values compared to the other clusters. Cluster 2, with 124 members, displays a broader range of age and height values compared to both Cluster 1 and Cluster 3. Cluster 3, consisting of 150 members, demonstrates age and height values that are higher than Cluster 1 but lower than Cluster 2 and Cluster 4. Finally, Cluster 4, encompassing 156 members, exhibits age and height values that are higher than those in the other clusters that many children are stunted based on standard anthropometric table for assessing children's nutritional status. The cluster optimization yielded four distinct clusters, which will serve as the input for identifying clusters during the data grouping process using the KM algorithm.
Deep learning–based real time speed limit sign detection with YOLOv12 on edge AI platforms for embedded ADAS Mohammed Chaman; Anas El Maliki; Youssef Natij; Hamad Dahou; Abdelkader Hadjoudja
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11128

Abstract

This research examined a real-time speed-limit sign detection framework based upon deep learning using the YOLOv12 neural network, optimized for the use of small edge devices that are embedded advanced driver assistance systems (ADAS). You only look once version 12 (YOLOv12) achieved a remarkable detection performance, while maintaining efficient computation, utilizing significantly optimized lightweight attention modules with an R-ELAN backbone capable of small and partially occluded detection. A custom dataset comprising 23,000 annotated images was prepared and augmented to ensure robustness under varying conditions. Model training utilized quantization-aware techniques and optimization via TensorRT and ONNX Runtime. Deployment and performance were rigorously evaluated on resource-constrained edge platforms, specifically NVIDIA Jetson Nano and Raspberry Pi 5. Experimental results demonstrated exceptional detection performance, achieving a precision of 99.0%, recall of 99.1%, and mean average precision (mAP@50) of 99.2%, confirming YOLOv12’s suitability for reliable, real-time ADAS implementation in intelligent transportation and autonomous vehicles.
Using LabVIEW software for remote voltage, current, and THD measuring and improving in the electrical grid Gentian Dume; Alfred Pjetri; Andi Hida
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8657

Abstract

The increasing penetration of photovoltaic (PV) systems has raised concerns about power quality due to the harmonic distortion injected during DC–AC conversion by inverters. These harmonics can interfere with nearby electrical devices and compromise grid stability. This paper presents a LabVIEW-based virtual instrument designed specifically for real-time monitoring, analysis, and mitigation of voltage and current harmonics in grid-connected PV systems. The core contribution of this work lies in the development of an interactive, software-based platform that not only measures total harmonic distortion (THD) in real time but also allows users to design and simulate harmonic filters dynamically. The system achieved a reduction of current THD by 52% and voltage THD by 73% under tested conditions, demonstrating its effectiveness in improving power quality. Unlike conventional hardware-dependent solutions, the proposed tool offers a low-cost, easily customizable, and scalable approach suitable for educational laboratories, field diagnostics, and smart grid applications. By enabling remote control and filter tuning through LabVIEW, this solution supports the growing demand for intelligent power quality management and facilitates the seamless integration of renewable energy into modern electrical grids.
Deep learning techniques business performance optimization in micro, small, and medium-sized enterprises: systematic review Carlos Roberto Sampedro Guaman; Miguel Angel Cano Lengua; Ciro Rodriguez Rodriguez; Igor Aguilar-Alonso
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9819

Abstract

The application of deep learning is transforming how micro, small, and medium-sized enterprises (MSMEs) operate. By using data-driven insights, these firms overcome traditional analytical limitations and improve decision-making. This study explores factors influencing deep learning adoption in MSMEs, identifies effective strategies, and compares performance between companies that implement these methods and those that do not. The objective is to analyze the impact of deep learning on optimizing the performance of MSMEs. The methodology consisted of a scientific review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) system and a bibliometric analysis to map international contributions. The results show that techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, transformers, and deep reinforcement learning (DRL) are crucial for marketing strategy prediction, customer experience personalization, and inventory management, leading to better return on investment (ROI), loyalty, and efficiency. Despite the potential benefits, there's still no enough research on how small businesses with limited resources use these methods and deal with issues like poor infrastructure and data access. Deep learning is essential for MSMEs' sustainability and competitiveness, even if there are challenges.
Optimized FOI-TD controller for automatic generation control of restructured power systems with sodium-ion batteries Nanthini B S; Ilanji Akilandam Chidambaram; Rajeswaran Sivasangari
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10220

Abstract

This article examines a novel control technique known as the fractional order integral-tilt derivative (FOI-TD) controller, which was used for automatic generation control (AGC) with a two-area restructured power system. The FOI-TD controller enhances performance by integrating tilt- derivative control with fractional-order control. Fractional-order control offers greater flexibility and allows for more precise tuning of the controller's response. The tilt component modifying the proportional term minimizes overshoot and improves settling times. The control parameters of the FOI-TD controller are optimized using the moth flame optimization (MFO) method and the efficacy of the given algorithm is associated with recent studies using meta-heuristic techniques. Analysis indicates that the MFO-optimized FOI-TD controller substantially enhances the dynamic response of the AGC loop and compared with traditional controllers. This enhancement includes reduced peak deviations, shorter settling times, and better suppression of area frequencies and tie-line power oscillations across different transaction scenarios within restructured power system. To further enhance AGC performance, fast-acting energy storage systems, like sodium- ion batteries (SIB), are integrated into the control area. The coordinated operation of the SIB units improves system dynamics and stability by mitigating initial frequency drops and tie-line power variations caused by sudden load disturbances.

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