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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 2,901 Documents
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Equilibrium optimizer-based double integral sliding mode maximum power point tracking for wind energy Sahu, Shrabani; Behera, Sasmita; Chandra Giri, Nimay; Uwadiegwu Alaneme, George; Abdelaziz Syam, Fathy; Salem, Fawzan
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.v14i6.10383

Abstract

Wind energy is an effective renewable energy source. However, when it comes to harnessing its power because of its variability and nonlinearity, traditional controllers have limitations. This work proposes the design of two nonlinear maximum power point tracking (MPPT) methods to track the maximum power point for stochastic wind in the below-rated wind speed zone. These methods are the sliding mode controller (SMC) and the double integral sliding mode controller (DISMC). A benchmark model of a 4.8 MW wind turbine (WT) is subjected to random wind profiles in the MATLAB/Simulink environment. The equilibrium optimizer (EO) is used here and contrasted with particle swarm optimization (PSO) and grey wolf optimizer to achieve a good design of the controller in the sliding plane and change the switching control in sliding mode. The proposed optimization methodology and DISMC improved the smoothening of the control of angular speed, and specifically, the EO outperformed the rest of the techniques.
Evaluating maintainability metrics in microservices-based student registration systems Gintoro, Gintoro; Cahyo Nugroho, Eko
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.v14i6.10457

Abstract

As governments redefine educational policy and schools evolve their priorities, more schools must have software that recalibrates with minimal friction. To provide objective guidelines, this study rigorously measures maintainability attributes in a microservices-styled student registration platform, framing the assessment with the ISO/IEC 25010 maintainability specification. We steered each of the standard's maintainability sub-characteristics into defined quantitative constructs, executed in the context of a production microservices topology. Architectural and behavioural views were analysed using Structure101 in static tool runs, and unified modeling language (UML) model inspection anchored the derivation of key metrics, ensuring that stakeholder-defined structures and live microservices concurrency both shaped the evaluation. Results indicate moderate system modularity with average component dependency (ACD) of 2.14, propagation cost (PC) of 10.2%, and identification of one non-trivial cycle group involving three classes. Cohesion analysis revealed structural improvement opportunities in core classes such as admin and candidate lack of cohesion in methods 4 (LCOM4)≥2). The inheritance structure shows optimal characteristics with shallow depth (depth of inheritance tree (DIT)≤1), and controlled breadth (number of children (NOC)=2), supporting both analyzability and modifiability. These findings provide actionable insights for enhancing system maintainability in microservices architectures, particularly for educational domain applications requiring frequent policy adaptations.
Bilateral transactions impact voltage stability and nodal pricing in power networks Wakte, Ganesh; Kumar, Mukesh; Aljaidi, Mohammad; Kumar, Ramesh; Singla, Manish Kumar
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.v14i6.9537

Abstract

This study investigates the impact of bilateral transactions on voltage stability and nodal pricing in the Indian power grid using a modified IEEE 30-bus system. A high voltage direct current (HVDC) link is integrated into the network to enhance control and system flexibility. Two advanced transmission pricing mechanisms— megawatt (MW)-Mile and megavolt-ampere (MVA)-Mile—are employed to allocate costs based on power flow magnitude and distance. The analysis incorporates hybrid AC-DC optimal power flow (OPF) modeling under various transaction levels. Simulation results show that a 100 MW bilateral transaction reduces the voltage at the receiving bus (bus 28) by 2% (from 1.05 to 1.03 p.u.) and increases the nodal price by 6.25% (from ₹4.80 to ₹5.10/kWh). The use of HVDC technology reduces total generation cost by approximately 8.2% (from ₹85 lakhs to ₹78 lakhs) and decreases real power loss from 70 MW to 50 MW. These findings confirm that bilateral transactions influence voltage profiles and market pricing. Moreover, MW-Mile and MVA-Mile methods demonstrate effective cost allocation capabilities. The proposed approach offers a practical framework for improving grid reliability and economic transparency in evolving power markets.
Advancements in machine learning techniques for precise detection and classification of lung cancer Abu Owida, Hamza; Arabiat, Areen; Al-Ayyad, Muhammad; Altayeb, Muneera
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.v14i6.10527

Abstract

Lung cancer remains one of the most prevalent and lethal malignancies worldwide, necessitating early detection and accurate classification for effective treatment. In this work, we present a unique machine learning (ML) model that uses medical imaging data to detect and classify lung cancer. Utilizing a dataset of 613 images which obtained from Kaggle, our model combines sophisticated feature extraction methods with three essential algorithms: AdaBoost, stochastic gradient descent (SGD), and random forest (RF). Orange3 data mining software was used to classify the model after it was preprocessed and features were extracted using MATLAB. Nonetheless, the model showed good performance in identifying lung cancer lesions in four different categories: squamous cell carcinoma, big cell carcinoma, adenocarcinoma, and normal. With an accuracy of 0.998 and an AUC range of 1.000, AdaBoost notably produced the best results. Overall, ensemble ML techniques demonstrated notable benefits over single classifiers, indicating its potential to aid in the creation of accurate instruments for the diagnosis of lung cancer in its early stages.
Designing an A+ LED solar simulator: spectrum optimization and its impact on silicon solar cells Boonmee, Chaiyant; Sritanauthaikorn, Patcharanan; Chudjuarjeen, Saichol; Kiatsookkanatorn, Paiboon; Wannakam, Khanittha; Homjan, Jeerawan; Sukthang, Kreeta; Suksing, Panet; Watjanatepin, Napat
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.v14i6.10877

Abstract

The development of light-emitting diode (LED)-based solar simulators that comply with the updated IEC 60904-9:2020 standard, particularly achieving a Class A+ irradiance spectrum, remains a significant challenge. This necessitates careful consideration of two key spectral quality indicators: spectral deviation (SPD) and spectral coverage (SPC). This study proposes a method to achieve a Class A+ solar simulator spectrum using a minimal number of LED types while optimizing SPD and SPC. It also examines the influence of SPD and SPC on the photogenerated current density (Jph) and short-circuit current density (Jsc) of crystalline silicon and multi-crystalline silicon solar cells. By selectively adding ultraviolet (UV) and near-infrared (NIR) LEDs to the original six-type LED configuration, the simulator’s spectral performance was enhanced to more closely align with the AM1.5G standard. The configuration incorporating both UV and NIR LEDs demonstrated the highest performance. It achieved an SPC of 97.521% and the lowest SPD at 26.088%. Simulation results confirmed that higher SPC and lower SPD values contribute to reduced errors in the calculated Jsc and Jph for both crystalline silicon (c-Si) and multi-crystalline silicon (mc-Si) solar cells. These findings highlight the importance of well-balanced spectral design and demonstrate that accurate spectral simulation is achievable using only essential LED wavelengths.
Embedded system with automatic control for solar energy capture using photovoltaic panels Rojas, Frank; Contreras, Fabrizzio; Alvarez, Juan; Yauri, Ricardo; Espino, Rafael
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.v14i6.10772

Abstract

Solar energy harvesting addresses challenges related to environmental variability and the limitations of fixed systems, which affect the energy yield obtained from photovoltaic panels. To improve efficiency, tracking systems are being developed using control algorithms or algorithmic energy management strategies. Reviewed research has explored methodologies such as software modeling, experimental testing, and integration of embedded systems with fuzzy logic and internet of things (IoT) for energy monitoring and management, using both single-axis and dual-axis technologies, demonstrating improvements in energy harvesting efficiency. This paper presents the development of a microcontroller-based system with automatic control to optimize solar energy capture in photovoltaic panels using light-dependent sensors and integrating control algorithms into low-cost hardware. The tests carried out demonstrated the operation of the tracking algorithm, confirming that the integration of light-dependent sensors, servo motors and the Arduino UNO microcontroller orient the solar panel based on the detected light, determining that with the generation of 900 mA with 6.98 V in full sunlight, the 5 V and 4400 mAh battery is charged, obtaining an autonomy of up to 3.65 days without solar recharging.
Artificial intelligence in smart home security: balancing innovation with ethics Sharah, Ashraf Al; Alawneh, Tareq A.; Owida, Hamza Abu; Alkasassbeh, Jawdat S.; Iqbal, Zahid
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.v14i6.9674

Abstract

Because of the evolution of artificial intelligence (AI), home security has progressed from a basic security system to an active architecture that is responsive and adaptive to real world situations. Due to the rapid adoption of AI in smart systems, there is increasing suspicion surrounding privacy issues and ethical ambiguity, as well as gaps when it comes to regulating these technologies. We provide an overview of AI in smart home security applications and examine the area of security, access control, intrusion detection, human action recognition, and research on intelligent automation. We summarize the last decade of evolution, with some summaries of previous on computer vision, authentication systems, and finding unusual patterns recently. Our key findings include the development of approaches to improve real time security monitoring, dramatic reductions in false alarms, and customization of home access using AI. Improvements in security have also increased risk with respect to ethical ambiguity as well as technical issues in certain cases. In this paper, we offers pathways for improved AI system design, proposed formal data protection regulations, and examples of simplifying complex system for user comprehension, which also establishes the groundwork for future efforts. Home security should balance new opportunities with ethical considerations.
Development of an internet of things microstrip antenna for turbo code off-grid emergency communications Jr., Fredelino A. Galleto; Africa, Aaron Don M.; Bedruz, Rhen Anjerome; Peradilla, Marnel; Barja, Samuelle P.; Macariola, Sean Bono L.; Manalo, Matthew Luigi N.; Ongkinglok, Angiolo C.
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.v14i6.10681

Abstract

Technologies and other things are fully automated, meaning signal processing and microstrip antennas for communication are essential because of their compact size and versatility. Due to inefficiency and other factors, traditional communication methods fail, which means some emergency communication systems encounter difficulties. MATLAB was used to simulate a microstrip antenna for turbo code off-grid emergency communication and signaling of internet of things (IoT) devices. A criterion is followed to determine whether a microstrip antenna’s behavior meets the emergency communication requirements. The results show that the system’s transfer function satisfies the required conditions to meet efficient communication and signaling, especially in emergencies. The step response peaked at 1.04 and an overshoot of 4.6%, meeting the conditions for efficient communication. Besides that, the generated Bode plot and Nyquist plot display the required behavior, meaning that the microstrip antenna can function as a communication device for emergency situations.
Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods Kassymova, Akmaral; Aibatullin, Tolegen; Yelezhanova, Shynar; Konyrkhanova, Assem; Mukhanbetkaliyeva, Ainur; Tynykulova, Assemgul; Makhazhanova, Ulzhan; Azieva, Gulmira
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.v14i6.10458

Abstract

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
New perspective in enhancing Papanicolaou-smear image using CLAHE and spider monkey optimization Khozaimi, Ach; Muharini Kusumawinahyu, Wuryansari; Darti, Isnani; Anam, Syaiful; Nahdhiyah, Ulfatun
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.v14i6.10250

Abstract

High-quality Papanicolaou (Pap) smear images are essential for reliable early detection of cervical cancer, yet low contrast and noise often hinder accurate interpretation. This study introduces spider monkey optimization (SMO)-contrast-limited adaptive histogram equalization (CLAHE), an optimized CLAHE framework guided by the SMO algorithm. A novel signal contrast (SC) objective function is proposed, combining perceptual enhancement contrast enhancement-based image quality (CEIQ) with fidelity preservation peak signal-to-noise ratio (PSNR) to adaptively tune CLAHE parameters. Experiments on the publicly available SIPaKMeD and Mendeley LBC datasets demonstrate that SMO-CLAHE consistently outperforms manual settings and flower pollination algorithm (FPA)-based optimization, and achieves performance comparable to pelican optimization algorithm (POA) across key quality metrics including entropy, structural similarity index (SSIM), PSNR, enhancement measure estimation (EME), root mean square contrast (RMSC), standard deviation (STD-DEV), and CEIQ. Furthermore, downstream evaluation using a MobileNetV3-S classifier shows that the enhanced images lead to improved cervical cancer classification performance. These results highlight SMO-CLAHE as a robust and clinically relevant preprocessing framework, offering a new perspective for Pap smear image enhancement and diagnostic support.

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