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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 3,202 Documents
 313   314   315   316   317 
Integrated-grid energy system for supplying reliable electricity to health sectors using grey wolf optimizer algorithms Olumuyiwa Ajibola Awoniyi; Evans Chinemezu Ashigwuike; Timothy Oluwaseun Araoye
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10778

Abstract

This study evaluates the techno-economic and environmental performance of various hybrid renewable energy system (HRES) optimized using the gray wolf optimization (GWO) algorithm, aimed at supplying reliable and sustainable energy to a health centre. Multiple system configurations were analyzed, including combinations of photovoltaic (PV), wind, diesel generator (DG), battery storage, and the electrical grid. Key performance metrics assessed include the cost of energy (COE), net present cost (NPC), payback period, annual utility bill savings, carbon dioxide (CO2) emissions, and annual fuel consumption. Results indicate that all hybrid systems significantly outperform the conventional grid-only base case across all evaluated parameters. The base case exhibits the highest COE ($0.159/kWh) and CO2 emissions (8,549 tons/year), underscoring the economic and environmental drawbacks of sole grid dependency. In contrast, the PV/wind/diesel/grid configuration achieved the lowest COE ($0.013676/kWh) and substantial reductions in emissions (2,349 tons/year), with a favorable payback period of 4.5 years. The optimization results highlight the effectiveness of GWO in balancing economic viability with environmental sustainability. Among the configurations, the PV/wind/grid and PV/wind/diesel/grid systems emerged as the most cost-effective and environmentally beneficial solutions. These findings highlight the potential of intelligently optimized hybrid renewable systems to deliver reliable, low-cost, and low-emission energy to critical infrastructure, such as healthcare centers.
Wind power forecasting under nonlinear conditions using fuzzy time series and long-short term memory models Vijayalakshmi Subramanian; Anuradha Chandrasekar; Padmajothi Varadarajan; Subha Sharmini Kannan; Lakshmi Dhandapani; Devaraj Vedhagiri
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.9041

Abstract

In the present day, there has been an increased focus on sources of clean energy, especially wind, since the depletion of fossil fuel reserves. Using the possibility of wind speed presents obstacles and complexities due to its non-linear characteristics. Therefore, a precise and effective wind energy forecast will greatly assist in resolving the system's operating and planning issues. The Forecasting of wind power is done through three forecasting techniques, the fuzzy time series (FTS) method, the long-short term memory (LSTM) and auto-regressive integrated moving average (ARIMA) method of forecasting. A comparative evaluation utilizing root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicates that the proposed FTS approach demonstrates superior performance of RMSE of 3.053 and a MAPE of 17.892% relative to both LSTM of RMSE of 5.378 and a MAPE of 32.844% and ARIMA of RMSE of 3.901 and a MAPE of 25.105%, attaining the minimal prediction errors. The outcomes show that FTS is effective well for datasets with seasonal changes and small training sizes.
Performance evaluation of superconducting fault current limiters for power system protection Yogesh Shivaji Pawar; Sandip Rahane; Amita Panchamrao Thakare; Dipalee M. Kate; Jyoti P. Rothe; Dinesh Suryakant Wankhede; Kirti Vaidya; Hema Kale; Rakesh G. Shriwastava; Rahul Mapari
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11568

Abstract

This paper presents a comprehensive assessment of the performance of superconducting fault current limiters (SFCLs), emphasizing their capability for rapid and effective mitigation of severe fault currents. With the increasing global demand for electrical power, the occurrence of system faults has become more frequent, resulting in high fault currents that generate significant mechanical and thermal stresses. These stresses can compromise the integrity of power system components, including transformers and associated equipment. Conventional methods of fault mitigation often lack adaptability and responsiveness to varying fault conditions. In contrast, the SFCL serves as an efficient stabilizing device, offering superior performance by rapidly limiting fault currents within the first cycle and thereby enhancing the transient stability of the power system. This study examines fault scenarios such as single line-to-ground (L-G), double line-to-ground (L-L-G), and three-phase-to-ground (L-L-L-G) faults, with particular focus on the role of SFCLs in reducing the operational burden on circuit breakers and improving overall system reliability. This study evaluates the performance of SFCL under multiple power system fault scenarios using simulation analysis. The results show that SFCL effectively limits fault current, enhances transient stability, and reduces mechanical and thermal stress on circuit breakers, improving overall grid reliability.
Enhancing image classification accuracy with AL-CNN: a hybrid of AlexNet and LeNet architectures Monika Chawla; Rashmi Agrawal; Bharat Bhushan
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11113

Abstract

Image classification is a key application of computer vision with direct relevance to medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of an adaptive learning convolutional neural network (AL-CNN) for image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation. The AL-CNN architecture integrates convolutional, pooling, and fully connected layers. The model was systematically trained on a subset of the dataset and subsequently tested on an independent validation subset to evaluate its efficiency and generalization capability. In addition, optimization techniques such as data augmentation, dropout, and advanced activation functions were employed to further enhance model performance. The results, based on accuracy metrics, indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification. This study demonstrates the potential of the AL-CNN approach to address various complexities in image classification, thereby enabling further innovation in this domain.
Energy-efficient flip-flop design using sense amplifier and clock gating techniques for next-generation IoT nodes Mahendrakan Kantharimuthu; Paulchamy Balaiah; Prema Selvaraj; Kalamani Chinnappa Gounder
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11873

Abstract

A clock-gated sense amplifier-based flip-flop (SAFF) that can operate dependably over a wide voltage and temperature range is suggested in the research paper. The proposed flip-flops (FF) drastically lowers the power and space requirements. Throughout all input data activity variations dependable and low-power operation is made possible by the single-ended latch design and the modified sense amplifier. The design utilization of the proposed SAFF which is made with 90 nm and 65 nm complementary metal-oxide-semiconductor (CMOS) technology is verified by a systematic and conclusive analysis using corner instance simulation for significant process, voltage, and temperature (PVT) variations. In addition, when the data is not changing a clock gating mechanism is employed to minimize the unwanted transition and lower power consumption. When SAFF and clock gated sense amplifier-based flip-flop (CGSAFF) are compared across various CMOS technologies the CGSAFF achieves a much lower delay than the traditional SAFF. The delay decreases from 47 to 6 ps in 90 nm technology and from 60 to 7 ps in 65 nm technology. This shows that CGSAFF outperforms SAFF in terms of speed performance and power efficiency making it more appropriate for portable internet of things (IoT) applications that require low power and high speed.
Detection of hoax content using support vector machine, Naive Bayes, and random forest algorithms Rama Sahtyawan; Anton Yudhana
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11351

Abstract

The rapid dissemination of hoax information through digital media poses a major danger to public perception and societal order. Automated detection systems are crucial to combat this issue. This research providing a side-by-side evaluation of three machine learning (ML) models: support vector machine (SVM), Naive Bayes (NB), and random forest (RF) for classifying news text as hoax or non-hoax. A total of 4,009 articles were first cleaned, broken into tokens, stripped of common stopwords, and lemmatized. Next, the term frequency–inverse document frequency (TF-IDF) approach was employed to pull out the key features. Evaluation of the models relied on metrics such as accuracy, precision, recall, and F1-score. The top results came from recorded by SVM, which reached 97% accuracy and a balanced F1-score of 96%, followed by RF at 95% accuracy. Meanwhile, NB only achieved 87% accuracy and showed a stronger tendency toward false positives. These results lead to the conclusion that SVM is the optimal algorithm for hoax news detection, thanks to its superior and well-balanced classification capability. What sets this study apart is its use of both comparative performance evaluation and statistical testing on an English-language dataset, providing a robust methodological foundation for future development and adaptation of hoax detection systems in other linguistic contexts, including Indonesian.
Comprehensive harmonic performance of MPPT-integrated SPWM controlled PV-fed multilevel inverter Swapna Subudhiray; Smrutiranjan Nayak; Priyattama Moharana
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11930

Abstract

This paper presents a comprehensive harmonic performance and robustness evaluation of a photovoltaic (PV)-fed cascaded H-bridge multilevel inverter (CHB-MLI) integrated with a perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. The proposed inverter is controlled using sinusoidal pulse width modulation (SPWM) to produce a high-quality AC output suitable for renewable energy applications. An inductor–capacitor (LC) filter is incorporated to reduce switching harmonics and improve output voltage quality. Harmonic analysis indicates that the total harmonic distortion (THD) of the inverter output voltage is reduced from 17.40% to 3.50% after filtering, which satisfies the IEEE 519 standard for low-voltage systems. Furthermore, a comparative evaluation with selective harmonic elimination (SHE) and space vector pulse width modulation (SVPWM) techniques highlights that SPWM provides a practical trade-off between harmonic performance and implementation complexity. The results confirm that the proposed PV-fed multilevel inverter (MLI) architecture offers reliable and efficient performance for renewable energy applications.
Tourists' perceptions of Ubud as a world gastronomy destination: a modified ABSA-BERT analysis Ni Wayan Sumartini Saraswati; Ketut Jaya Atmaja; I Wayan Dharma Suryawan; Christina Purnama Yanti
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11411

Abstract

Although Ubud has been designated by the United Nations World Tourism Organization (UNWTO) as a global gastronomy tourism destination, existing research remains limited in assessing its strengths and challenges from a data-driven tourist perspective. Prior studies largely rely on qualitative, supply-side approaches and conventional sentiment analysis that overlook specific experiential aspects. Moreover, the integration of multi-platform data and the application of modified deep learning-based aspect-based sentiment analysis (ABSA) models remain underexplored in gastronomy tourism. This study addresses these gaps by employing a modified ABSA-bidirectional encoder representations from transformers (ABSA-BERT) framework on data from TripAdvisor and social media X to provide a more comprehensive evaluation of tourist perceptions. The model incorporates predefined aspect-based aspect term extraction (ATE), valence aware dictionary and sentiment reasoner (VADER) lexicon labeling, and a novel algorithm for extracting aspect-level opinions. The results demonstrate strong performance, achieving 93.50% accuracy on X data and 91% on TripAdvisor. Findings indicate an overall positive perception of Ubud as a global gastronomic destination, particularly for its authentic local cuisine and appealing natural dining atmosphere, although challenges such as overly spicy dishes and uncomfortable seating arrangements remain.
Enhancing anomaly detection in video surveillance with spatio-temporal enhanced deep associative memory networks Kusuma Sriram; Kiran Purusotham; Vinutha Gurulingaiah Kaelgaerae
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10344

Abstract

In the period of extensive video data generation from various surveillance sources, ensuring public safety and security is vital. Detecting unusual crowd behavior is essential, especially in scenarios with large gatherings. However, despite widespread video surveillance, incidents like vehicular accidents, stampedes, and burglaries still occur due to the limitations of traditional surveillance systems. In anomaly detection, finding the critical deviated pattern is the main and critical task. In the context of intelligent video surveillance, automated detection of abnormal behavior is achieved through computer vision analysis, eliminating the need for constant human monitoring. This paper proposes the spatio-temporal enhanced deep associative memory networks (STEAD)-network, a novel approach for anomaly detection in video sequences. The STEAD-network combines various techniques, including spatio-temporal enhancement, associative memory modules, and pattern recognition, to effectively capture and recognize abnormal events. Three benchmark datasets, UCSD Ped2, CUHK Avenue, and ShanghaiTech are used to evaluate this proposed dataset and to compare with existing state-of-the-art techniques. The results demonstrate that the STEAD-network consistently outperforms other methods, achieving significant improvements in anomaly detection accuracy across all datasets. The development of intelligent video surveillance systems is aided by this research by enhancing their ability to autonomously and accurately detect abnormal behavior in real-world scenarios.
Wideband bow-tie slot antenna with defected ground structure for efficient radio frequency energy harvesting Usman Yau; Jun Jiat Tiang; Surajo Muhammad; Nazih Khaddaj Mallat; Isiyaku Yau
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11329

Abstract

This work proposes the design and performance assessment of a wideband bow-tie slot antenna specifically developed for radio frequency energy harvesting (RFEH). The design employs a self-complementary bow-tie configuration, integrated with a defected ground structure (DGS) and fractal elements, to achieve improved bandwidth (BW) and gain. It achieved -10 dB simulated (measured) BW of 2.50 GHz (2.10 GHz) over a frequency span of 0.80-3.30 GHz (0.80 to 2.90 GHz) respectively. The results cover a target fo, simulated and measured fractional bandwidth (FBW) of 122% and 114% respectively, with a peak realized gain of 6.42 dBi. The antenna also achieved a compact dimension of 126 mm × 90 mm (0.73 lamda g × 0.52 lamda g). It is finally integrated with a wideband RF-rectifier for the purpose of validation. The design enables continuous wireless power transfer (WPT) suitable for RFEH in internet of things (IoT) devices and sensor nodes.

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