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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.
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Articles 75 Documents
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Search results for , issue "Vol 14, No 6: December 2025" : 75 Documents clear
Influence of installing a virtual synchronous generator control on Lombok Island power grid with high penetration of PV plants Setiadi, Herlambang; Mithulananthan, Nadarajah; Nuris Syifa, Baity; Ricky Ananda, Yoshiko; Cahya Anugrah Haebibi, Riski; Afif, Yusrizal
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.v14i6.9733

Abstract

Indonesia is a country with several islands, and providing clean energy in islanded power systems connected to a single main grid would be economy challenging. On the other hand, absence of inertia, system strength, and damping value in islanded power systems due to inverter interfaced renewable energy (RE) resources can cause significant decline of power system stability. The primary concern with integrating large scale photovoltaic (PV) power plant in an islanded power system is maintaining frequency and voltage stability. This research investigates the application of virtual synchronous generator (VSG) in Lombok’s Islanded power system, considering high penetration of PV. A thorough time domain simulation is performed with a detailed modelling of power system in Lombok Island to study the dynamic voltage and frequency stability. The simulation results show that the VSG improves both frequency and voltage stability in transient and steady state stages, ensuring smoother operation and faster stabilization time. It is found that the frequency deviation can be curtail up to 0.5% and the steady state can be increased up to 0.1%.
Intelligent building automation system using ESP32, Azure and internet of things technologies Cardoza, Fernando; Samamé, Cristy; Yauri, Ricardo; Castro, Antero
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.v14i6.11021

Abstract

The adoption of home automation systems in buildings faces limitations due to their cost, integration complexity, and protocol heterogeneity, which hinders the development of accessible solutions based on embedded devices to improve interaction in environments within buildings or homes. The literature review indicates that the selection of hardware and communication protocols in home automation systems considers factors such as cost, available infrastructure, and application context. In addition, approaches are identified that prioritize security, wired or wireless connectivity, and affordability. This paper presents the development of an affordable home automation system for building automation in Lima, using the ESP32 microcontroller and internet of things (IoT) technologies. The objectives focus on hardware design, implementation of control algorithms, remote monitoring interface, and validation in a simulated environment. The solution includes Wi-Fi connectivity, a cloud-based MySQL database, and a web interface. Key findings include the home automation system, integrated with Flask technology and web services, enabling monitoring and control via a responsive web interface, demonstrating its operability and ensuring lossless data transmission.
Route splitting and adaptive mutation in genetic algorithms for the capacitated vehicle routing problem Kadyrov, Shirali; Turan, Cemil
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.v14i6.9204

Abstract

The capacitated vehicle routing problem (CVRP), where vehicle capacity constraints limit the load carried per route for multiple vehicles, is addressed using an optimized genetic algorithm (GA) framework. This work focuses on finding the best configuration of GA by systematically evaluating 12 distinct GA variants, differing in adaptive mutation rates and route-splitting strategies. The framework integrates adaptive mutation rates and novel route-splitting approaches—greedy, dynamic programming (DP), and heuristic—to enhance computational efficiency and solution quality. Experiments on six CVRP instances of varying complexity, encompassing differences in problem size, vehicle capacity, and geographical distribution, demonstrate the heuristic approach’s effectiveness. It achieves solutions within 2%–5% of the optimal cost of DP while being 3–4 times faster. Adaptive techniques reduce costs by up to 20% compared to standard GAs and heuristics. The framework’s scalability is evident in large-scale instances such as the 200-customer case, where the heuristic method balances cost (414.17) and computation time (0.003 seconds). The developed software is openly available at GitHub, providing a robust tool for addressing practical logistics challenges.
Brain tumor classification using PCA-NGIST features with an enhanced RELM classifier Babu, Bukkapatnam Rakesh; Rajesh, Vullanki; Rajanna, Bodapati Venkata; Ahammad, Shaik Hasane
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.v14i6.10742

Abstract

Brain tumours may cause severe health risks because of abnormal cell growth, which may result in organ malfunctions and death in adulthood. As precise identification of the tumour type is required for effective treatment. Magnetic resonance imaging (MRI) has recently been provided as an effective method for brain tumour diagnosis by computer-based based systems. To categorize brain tumours from MRI images, the paper offered a fusion model integrating an enhanced regularized extreme learning machine (RELM) classifier with principal component analysis (PCA) and normalized GIST (NGIST) feature extraction. While NGIST extracts strong spatial and texture features essential for modelling the tumour, PCA reduces the dimension of the input features without sacrificing significant data patterns. The improved RELM efficiently categorizes brain tumours into three categories: pituitary, meningioma, and glioma. It is optimized to improve learning capacity and generalization. The novelty of this study lies in the integration of NGIST descriptors with PCA-driven dimensionality reduction and an enhanced RELM classifier in a single lightweight framework. Unlike conventional methods that trade accuracy for computational cost, the proposed model ensures high precision and recall while remaining computationally efficient. This unique fusion demonstrates significant improvements in both diagnostic accuracy of 96% and clinical applicability, offering a balanced solution for real-time brain tumor classification.
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.

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