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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 75 Documents
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Search results for , issue "Vol 14, No 6: December 2025" : 75 Documents clear
Design and analysis of an asymmetrical star-shaped fractal antenna with meta-surface integration at 5.2 GHz Dalsania, Piyush; Rathod, Jagdish M.
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10163

Abstract

Wireless communication requires optimized antenna designs to ensure maxi mum signal reception and transmission in today’s rapidly advancing technolo gies. Recent research emphasizes improving antenna efficiency and directivity to support higher data rates, extended coverage, and reliable connectivity. How ever, conventional antenna structures often suffer from narrow bandwidth, low radiation efficiency, and high return loss, which degrade signal quality and re strict operational range, particularly in complex electromagnetic environments. This study introduces an innovative asymmetrical star-shaped fractal antenna coupled with a metasurface layer consisting of periodic split-ring resonator (SRR) unit cells on a FR4 substrate to overcome these restrictions. The SRR based metasurface plays a critical role in suppressing surface waves, improving impedance matching, and enhancing radiation directivity. Experimental evalu ations were performed across 4.5–10 GHz, focusing on key performance mea sures such as gain, return loss, and voltage standing wave ratio (VSWR). The suggeted antenna achieved a stable return loss below −10 dB and demonstrated a strong operational peak at 5.2 GHz, with improved directivity and radiation efficiency compared to conventional patch designs. The integration of asym metrical star-shaped fractal geometry with SRR-based metasurface technology effectively addresses the shortcomings of traditional antennas, establishing the proposed design as a compact, efficient, and reliable candidate for mid-band wireless communication systems.
Meta-learning for malaria diagnosis: evaluating stacking models for enhanced classification performance Napa, Komal Kumar; Murugan, Sangeetha; Subramanian, Sathya; Saravanan, Durga Devi; Nageswari, Devana; Prasad, Battula Krishna
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10158

Abstract

Accurate malaria detection is crucial for effective disease management, particularly in regions with limited medical resources. Deep learning models have shown promising results in automated diagnosis, yet real-world deployment often faces challenges such as computational cost and model interpretability. This study evaluates multiple deep learning architectures—VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121—on the publicly available National Institutes of Health (NIH) malaria cell image dataset (27,558 images), and enhances their performance using stacking ensemble learning with different meta-learners. Among individual models, DenseNet121 achieved the highest accuracy of 88.00%, while MobileNetV2 had the lowest at 84.80%. Implementing stacking with logistic regression as the meta-learner improved accuracy to 89.40%, while random forest increased it to 90.10%. The best performance was achieved with XGBoost as the meta-learner, attaining an accuracy of 91.20%, precision of 92.10%, recall of 90.80%, and an F1-score of 91.40%—representing a 3.2% accuracy improvement over the best individual model. The classification report further confirms superior performance in distinguishing infected and uninfected cases. These results highlight the potential of stacking with advanced meta-learners to support health workers in rapid, reliable malaria diagnosis, ultimately aiding timely treatment, and improving patient outcomes in clinical and field settings.
Thermal analysis of li-ion battery pack using phase change materials based on climate conditions Mohammed Mokhtar Benounnane, Ishak; Wahid Belarbi, Ahmed; El Bachir Ghribi, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9641

Abstract

The development of lithium-ion batteries necessitates improved management of these systems, particularly with regard to thermal aspects. They operate optimally between 35 °C and 45 °C. Temperatures exceeding 50 °C accelerate cell aging, while those surpassing 60 °C can trigger thermal runaway, potentially leading to catastrophic failure. To mitigate these risks, phase change materials (PCMs) are employed in battery thermal management systems (BTMS). They absorb heat during charging or discharging, transitioning from solid to liquid, then release the stored energy during periods of low demand, solidifying to help regulate battery temperature. This study conducts a thermal analysis of a lithium-ion (LiFePO4) battery pack delivering a 24 V load, using COMSOL MULTIPHYSICS software. The objective is to evaluate and compare the thermal behavior of different PCMs, RT27, Paraffin Wax 58-60, and HM030, against air as a baseline reference. Simulations are performed using the integrated finite element method (FEM), with a discharge rate of 4 C. A correlation is proposed between the choice of PCM and the climate in specific locations, with the choice being made based on the disparities in the results obtained.
Design and emulation of an SDN network with opendaylight to improve QoS in a peruvian financial institution Roncal, Juan David Indigoyen; Paulino, Christian Ovalle
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9895

Abstract

This study presents the design and emulation of a software-defined networking (SDN) architecture using the OpenDaylight controller to enhance the quality of service (QoS) in a Peruvian financial institution. The main objective is to overcome limitations of traditional networks, including high latency, limited bandwidth, and packet loss, which hinder the efficiency of financial services. The proposed SDN architecture was implemented and tested through simulations in the Eve-NG platform, where key performance parameters—latency, throughput, and packet loss—were measured. Results demonstrated significant improvements, with latency reduced by up to 40%, stable throughput maintained at 10 Mbps across all branches, and a noticeable reduction in packet loss. These outcomes validate the feasibility of adopting SDN in financial environments to support critical services and ensure operational continuity. Furthermore, the findings emphasize SDN’s role in modernizing network infrastructures, improving user experience, and aligning local financial institutions with international technological trends. Future research may explore alternative SDN controllers, scalability in larger topologies, and integration with emerging technologies such as network function virtualization (NFV).
Research on PMSM control without speed sensorless applied to industrial electric drive system based on ADSMC method Van Chinh, Ha; Duc Chuyen, Tran; Hoai Nam, Nguyen
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10266

Abstract

The paper research, calculates, and designs an industrial electric drive system control such as: computer numerical control (CNC) machining machines, milling machines, and grinding machines, with sensorless permanent magnet synchronous motors (PMSM) based on measuring current components, axial position and applied voltage to obtain information about rotation angle and speed for PMSM based on adaptive sliding mode control (ADSMC) method. Here an optimal sliding surface will be designed to demonstrate faster convergence than conventional sliding mode control. Then, an adaptive law is researched and developed to make the control parameters, especially the switching gain, updated quickly online. Therefore, the motor noise can be effectively reduced and the system can be better eliminated from noise, Chattering, and nonlinear noise. Finally, a reference model was created, the exponential decay curve was applied to track the angular position error. The ADSMC system with model reference proposed by the authors in the paper has combined the advantages of sliding mode control method and adaptive control method according to the sample model. The simulation results show that the performance is achieved faster and the control process is more accurate, the error of speed and angular position (less than 0.01%) compared to other control methods.
Cybersecurity challenges in healthcare: mitigating risks in a rapidly evolving digital landscape Arina, Alexei; Bolun, Ion; Alexei, Anatolie
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9685

Abstract

The rapid digital transformation of the healthcare sector brings significant benefits but also exposes institutions to escalating cybersecurity risks. This study analyzes vulnerabilities such as ransomware, supply chain compromises, and insider threats, drawing on international reports from World Health Organization (WHO), Healthcare Information and Management Systems Society (HIMSS), Ponemon, and Verizon. The paper contributes a unique mitigation framework that consolidates three strategic pillars: i) continuous cybersecurity training for medical staff, ii) deployment of advanced technological safeguards, and iii) establishment of collaborative incident reporting mechanisms. Beyond mapping current threats, the study provides policy-oriented guidance for strengthening resilience in both developed and emerging healthcare systems. With healthcare breaches costing an average of $10.1 million per incident, the findings highlight the urgent need for coordinated action to ensure patient safety, service continuity, and institutional trust.
Feature selection to predict COVID-19 new patients in the four southern border provinces of Thailand Photphanloet, Chadaphim; Shuaib, Sherif Eneye; Ritraksa, Siriprapa; Riyapan, Pakwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9068

Abstract

This paper presents a machine learning-based prediction framework that utilizes ensemble feature selection techniques to accurately forecast the number of new coronavirus disease (COVID-19) infections in Thailand’s four southern border provinces. The framework used include multiple linear regression (MLR), mul tilayer perceptron neural networks (MLP-NN), and support vector regression (SVR), to classify short-term trends in new patient cases. The study evaluates the effectiveness of these models across different provinces and demonstrates how integrating feature selection methods: forward selection (FS), backward elimination (BE), and genetic algorithms (GA) enhances prediction accuracy. The findings highlight the adaptability of the models, with each province ben efiting from tailored model-feature selection strategies. The results show that the predictive models align closely with real patient data, enabling authorities to anticipate outbreaks and implement timely interventions. Moreover, the pro posed methodology can be applied to other epidemics, making it a valuable tool for public health planning and preparedness. The study offers actionable in sights for decision-makers, emphasizing the importance of predictive modeling in community-level outbreak management.
Enhancing cloud resource management: leveraging adversarial reinforcement learning for resilient optimization Dwinggo Samala, Agariadne; Rawas, Soha; Criollo-C, Santiago
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10636

Abstract

This paper introduces the first adversarial reinforcement learning (ARL) framework for resilient cloud resource optimization under dynamic and adversarial conditions. While traditional reinforcement learning (RL) methods improve adaptability, they fail when faced with sudden workload surges, security threats, or system failures. To address this, we propose an ARL-based approach that trains RL agents using simulated adversarial perturbations, such as workload spikes and resource drops, enabling them to develop robust allocation policies. The framework is evaluated using synthetic and real-world Google Cluster traces within an OpenAI Gym-based simulator. Results show that the ARL model achieves 82% resource utilization and a 180 ms response time under adversarial scenarios, outperforming static policies and conventional RL by up to 12% in terms of cost-effectiveness. Statistical validation (p0.05) confirms significant improvements in resilience. This work demonstrates the potential of ARL for self-healing cloud schedulers in production environments.
Iris-based lung cancer pre-scanning for mobile platforms Ho-Dac, Hung; Anh Le, Tuan; Thua Huynh, Trong
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11146

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality globally, with early detection being critical for improving survival rates. Traditional diagnostic methods such as computed tomography (CT) scans and biopsies are effective but often costly, invasive, and inaccessible in resource-limited settings. In this study, we evaluate suitable deep learning models for mobile platforms and propose an application for early detection of lung cancer based on iris images. Through experimentation and comparison, the results show that the MobileNet model family achieves high performance while maintaining a light-weight architecture. The positive results of this study further strengthen the potential application of iris in the pre-diagnosis of lung cancer via mobile platforms.
Prediction of asphalt performance based on plastic waste using machine learning Agung Ananda Putra, I Gusti; Rokade, Darpan
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10598

Abstract

The incorporation of plastic waste into asphalt mixtures offers a promising solution to address the growing environmental concerns while enhancing the performance of road materials. Traditional methods, such as the Marshall test, are costly and time-consuming, thus highlighting the need for more efficient prediction techniques. Machine learning (ML) models, including random forest (RF), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), have shown significant potential in predicting asphalt performance, optimizing material compositions, and reducing the dependence on labor-intensive laboratory tests. Key influencing factors such as bitumen content, plastic size, and temperature have been identified as crucial for improving asphalt properties. This systematic review emphasizes the potential of ML in streamlining the development of plastic-modified asphalt, offering a sustainable and cost-effective approach to road construction. Furthermore, it supports the advancement of green infrastructure and lays the foundation for future innovations in sustainable pavement engineering, contributing both to academic research and practical applications in the construction industry.

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