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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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Secure and efficient elliptic curve-based certificate-less authentication scheme for solar-based smart grids Bari Shovon, Reduanul; Mohammad, Ashif; Das, Rimi; Hossain, Tuhin; Ahasun Habib Ratul, Md; Kundu, Ronjon; Ahsan Arif, Md.
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8830

Abstract

Solar-based smart grids have emerged as a transformative force, encapsulating a paradigm shift towards decentralized and sustainable power generation. However, this evolution is accompanied by growing concern-authentication challenges that pose a substantial threat to solar-based smart grids' security. Existing work done by researchers reveals a gap in addressing these authentication issues, resulting in vulnerabilities that compromise the overall security and performance of solar enabled smart grid infrastructures. In response to these concerns, this paper suggests a novel certificate-less authentication scheme designed explicitly for solar-based smart grids. Our technique, which uses elliptic curve (EC) encryption, mitigates authentication problems and navigates the resource limits inherent in a smart grid environment. The security evaluation also shows that our mechanism security is higher in terms of the security attributes it delivers. Supported by a Scyther-based protocol specification, our solution undergoes a rigorous security analysis, demonstrating its robustness and effectiveness in critical security attributes. Furthermore, a performance evaluation underscores the efficiency of our scheme, positioning it as a robust, and effective solution for fortifying solar-based smart grid environments against evolving cyber threats.
Artificial intelligence in vestibular disorder diagnosis Ben Slama, Amine; Sahli, Hanenne; Amri, Yessine; Labidi, Salam
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7160

Abstract

Vertigo is a prevalent symptom of vestibular disorders, with ocular nystagmus analysis serving as a key indicator for distinguishing between peripheral and central vestibular conditions. Videonystagmography (VNG) provides objective and reliable measurements, making it a valuable tool for clinical assessments. However, the complexity and variability of vestibular diseases pose challenges for conventional VNG methods, such as caloric, kinetic, and saccadic tests, in accurately identifying vertigo subtypes. Traditional diagnostic approaches often fail to fully utilize nystagmus characteristics in correlating with specific vestibular disorders, limiting their effectiveness. Recent advancements in artificial intelligence (AI), particularly deep learning and machine learning (ML), offer promising solutions for improving vertigo diagnosis. These technologies facilitate automated, rapid, and precise analysis by extracting relevant clinical features and classifying vestibular disorders with higher accuracy. ML-based models enhance diagnostic reliability, reducing human bias and subjectivity in assessment. This study reviews the latest research on feature extraction and ML applications in vertigo diagnosis, emphasizing their potential to revolutionize clinical decision-making. It aims to provide a comprehensive understanding of AI-driven approaches and their role in advancing vertigo analysis, paving the way for more effective diagnostic methodologies in the future.
Improved half-maximal inhibitory concentration regression model using amyotrophic lateral sclerosis data Selvaraj, Devipriya; M S, Vijaya; Sakkarapani, Krishnaveni
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8520

Abstract

The current research addresses the critical need for precise half-maximal inhibitory concentration regression in the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Unavailable drug-induced gene expressions and irrelevant molecular descriptors have yielded regression models with less accuracy using traditional machine learning (ML). Drugs can be converted to graph format and integrated with gene expressions to learn drug-gene interactions better thereby producing precise half-maximal inhibitory concentration regression models. To accomplish this, three variants of graph neural networks (GNN) namely graph attention networks (GAT), message passing neural networks, and graph isomorphism networks are utilized in the proposed work. The gene expression profiles of ALS drugrelated genes were retrieved from the DepMap PRISM drug repurposing hub, and the drug graphs with their accompanying half-maximal inhibitory concentration values were obtained from the ChEMBL databases. The graph is constructed for ninety approved drugs connected to 32 key protein targets of ALS and its related conditions. The half-maximal inhibitory concentration regression model trained with optimized hyperparameters in GAT performs well with an R2 score of 0.92, a mean absolute error (MAE) of 0.20, and a root mean square error (RMSE) of 0.17. This model produced better results than other ML and deep learning models.
Comparison of multilayer perceptron and nonlinear autoregressive models for wind speed prediction Kacimi, Houda; Fennane, Sara; Mabchour, Hamza; ALtalqi, Fatehi; El Moury, Ibtissam; Echchelh, Adil
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8541

Abstract

Wind energy is a critical component of the global shift to renewable energy sources, with significant growth driven by the need to reduce carbon emissions. Accurate wind speed prediction is crucial for increasing wind energy output since it directly influences wind farm design and performance. The purpose of this study is to compare two artificial neural network (ANN) models for predicting wind speed in Dakhla City, a place with a high wind energy potential. The first model is a multilayer perceptron (MLP) trained with the backpropagation algorithm, while the second is a nonlinear autoregressive with exogenous inputs (NARX) model, a form of recurrent neural network (RNN) noted for its ability to handle time-series data more well. The comparative analysis results show that the NARX model outperforms the MLP model in terms of wind speed forecast accuracy. The NARX model achieved a near-perfect regression coefficient (R) of 0.9998 and a root mean square error (RMSE) of 1.02899, indicating that it can represent complex, nonlinear wind speed patterns. These findings indicate that the NARX model could be a beneficial tool for increasing the efficiency of Dakhla City’s wind energy infrastructure, assisting the region in meeting its renewable energy ambitions.
Application of JAYA algorithm for optimizing allocation and size of thyristor-controlled series compensator devices Nhan Bon, Nguyen; Le, Thanh-Lam
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8900

Abstract

Electricity serves as the backbone and essential energy source for various sectors, including transportation, residential areas, manufacturing, and industry. As engineering and technology advance, the demand for electricity continues to rise. Expanding the electricity grid to meet transmission needs and provide high-quality service has become a fundamental challenge in the power system domain. However, load expansion introduces issues such as line overloads when demand surges, compromising power quality, system security, and reliability during operation, potentially leading to system failures. Addressing these load-related problems is crucial for enhancing power system stability, reducing troubleshooting expenses, and improving operational efficiency. This study proposes the utilization of thyristor-controlled series compensator (TCSC) as a solution to enhance power system efficiency. Furthermore, to optimize TCSC placement and determine the appropriate compensation level for devices on transmission lines, the research suggests employing the JAYA optimization algorithm. MATLAB software is utilized to investigate the IEEE standard 30-node transmission lines case. The obtained results have demonstrated the effectiveness of the solution in enhancing electrical transmission capacity, improving stability, and reducing energy losses within the system at a low operational cost.
Solar panel installation feasibility analysis based on techno-economic of PVSyst at Universitas Multimedia Nusantara Rinanda Saputri, Fahmy; Dimas Paramasatya, Johanes; Maliki Akbar, Agie
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9135

Abstract

Universitas Multimedia Nusantara (UMN) integrates green building principles to enhance environmental sustainability by reducing energy waste and utilizing renewable energy sources. This study conducts a feasibility analysis of installing solar panels in a green open space near building D to supply up to 20% of its electricity needs. PVSyst simulations evaluated different panel orientations (north, south, and east). The results indicated that the installation is currently unfeasible, with a net present value (NPV) of -134,346,450.22 IDR and an internal rate of return (IRR) of -4.64%. The challenges included shading from surrounding buildings, heat buildup, and limited installation space. To improve viability, future installations should focus on sites with minimal shading and explore advanced technologies to enhance efficiency. Additionally, optimizing panel orientation and investigating alternative renewable energy sources suited to UMN’s conditions are crucial. These measures can enhance the effectiveness of solar installations and contribute to overall energy sustainability on campus.
Multiword target-independent transformer-based model for financial sentiment analysis in colloquial Cantonese Chun Fai Chu, Carlin; So, Raymond; Kan Lam Kwong, Ernest; Chan, Andy
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8963

Abstract

Tokenization process decomposes a multi-word-span instrument name into several tokens and the transformer attention mechanism handles each token individually, thus hindering the treatment of the related tokens as a single entity. The existence of multiple instruments in a single message further exaggerates the complications and results in low predictive performance. This study proposed the use of sequentially tagged target-independent sentinel tokens to encapsulate multiword instrument aspects for natural language inference model fine-tuning. The encapsulation not only facilitated the attention mechanism to handle an instrument name as a single entity but also enabled the model to handle unseen instruments effectively. Our empirical analysis was based on 5,178 manually annotated instrument–sentiment pairs originated from finance discussion board messages that addressed sentiments of one to four instruments in a single post. The proposed approach consistently outperformed the direct bidirectional encoder representations from transformers (BERT) based approach in terms of recall, precision, and F1-score when handling financial commentaries written in colloquial Cantonese. This study demonstrated the potential benefits of target-independent sentinel token encapsulation for natural language inference. The underlying logic of multiword target-independent encapsulation was expected to hold for other languages, including Chinese, Japanese, and Thai.
Enhancing photovoltaic parameters based on modified puma optimizer Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Elsayed Abd Elaziz, Mohamed; Soleimanian Gharehchopogh, Farhad; A. Shehadeh, Hisham; Sabo, Aliyu; Prapanca, Aditya
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8977

Abstract

This article presents a photovoltaic (PV) optimization approach using the puma optimizer (PO) approach, which has been enhanced by utilizing Lévy flight optimization. The name of this approach is modified puma optimizer (MPO). PV generation systems are essential for sustainable solar energy utilization. It is an innovation and clean energy. There is an urgent demand for suitable and reliable simulation and optimization techniques for PV systems. This will result in increased efficiency. Algorithms with a high degree of reliability are needed to ensure optimal PV parameters. This study was conducted with MATLAB software. This article introduces the original PO method as a means to evaluate the performance of the MPO approach. The root mean square error (RMSE) function serves as a benchmark. Based on the simulation findings, the MPO approach shows superior RMSE compared to the PO method, specifically at a value of 0.0026%.
Durian plant health and growth monitoring using image processing Awang Ahmad, Zahari; Sie Chow, Tan; Muhammad Asmawi, Mohamad Akmal; Abdullah, Abu Hassan; Jack Soh, Ping
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8550

Abstract

The demand for durians has increased considerably, gaining significant popularity in the market. Under the Industrial Revolution 4.0, precision agriculture is expanding globally, utilizing a range of digital technologies to provide the farming industry with crucial information for enhancing farm productivity. For durians to produce high-quality fruit, it is essential that the plants receive sufficient nutrients. Therefore, it is crucial for farmers to monitor the growth rate of durian plants to ensure they receive suitable nutrients for optimum growth. Manual growth monitoring often yields inaccurate results and is prone to human error. Thus, automatic systems for plant image analysis could prove highly beneficial for practical and productive agriculture. This research utilizes the you only look once version 5 (YOLOv5) model alongside an image referencing method for growth monitoring. It begins with the detection of the durian tree, segmenting the leaf area and computing tree size through image referencing. This method achieves a precision of 96% in detecting durian trees from images. Through these images, the growth rate of the durian is assessed through comparisons of canopy growth, stem size, and tree height.
Creating and analysing privacy policies of Malaysia e-commerce using personal data protection act Shehu Ali, Auwal; Zaaba, Zarul Fitri; Mahinderjit Singh, Manmeet; Anuar, Nor Badrul; M. Shariff, Mohd Ridzuan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8991

Abstract

Despite legally binding agreements between users and website owners, users often overlook website privacy policies due to their length and complexity. Transparency in these policies is crucial, particularly in Malaysia, where regulatory agencies face challenges ensuring compliance with the personal data protection act (PDPA) of 2010 due to intricate language and complex legal clauses. Machine learning has been used to analyse privacy policies under various legal frameworks, but no dataset currently exists for the Malaysian PDPA. Thus, to bridge this gap, we introduce a pilot corpus of 50 privacy policies specifically tailored to the Malaysian PDPA. This dataset is analysed and made available for academic research, offering insights into privacy regulations and identifying trends in privacy policy transparency. Our findings pave the way for the development of tools to enhance compliance with PDPA standards and improve policy readability for users. The corpus also serves as a foundation for further research in privacy and data protection, encouraging the exploration of automated approaches for policy analysis and regulatory oversight.

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