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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 3: June 2025" : 75 Documents clear
Multilayer crypto method using playing cards shuffling operation J. Rasras, Rashad; Rasmi Abu Sara, Mutaz; Alqadi, Ziad
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.v14i3.9183

Abstract

An efficient and highly secure method of secret message cryptography will be presented which based on the principle of playing cards shuffling. The method will be implemented in a selected number of layers, each layer will encrypt-decrypt the input message using its own private key (PK), the output of any layer can be taken as a final encrypted-decrypted message, increasing the number of layers will increase the security level of the massage, making the hacking attacks impossible. In the encryption function a key generation and a message blocks shuffling will be executed, while in the decryption function the key generation and the message blocks reverse shuffling will be executed. The PK used in this method will be complicated and it will contain for each layer 2 chaotic parameters (r and x) and the block size (BS), utilizing these parameters, a chaotic logistic map model is run to produce a chaotic key, which is sorted to produce the layer's index key. Applying 4 layers the length of confidential key will be 768 bits, this length will be able to generate a large key space which is robust to hacking attempts. The speed parameters and throughput of the proposed will be calculated and compared with those of other methods.
Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection Kalluri, Venkata Saiteja; Malineni, Sai Chakravarthy; Seenivasan, Manjula; Sakkarai, Jeevitha; Kumar, Deepak; Ananthan, Bhuvanesh
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.v14i3.8757

Abstract

In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
A novel technical analysis and survey on disaster robots for flood search and rescue operations Duvvuru, Rajesh; Jagadeeswara Rao, Peddada; Narasimha Rao, Gudikandhula; Rayachoti, Eswaraiah; Boyidi, Suribabu; Prathyusha, Kodamala
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.v14i3.7364

Abstract

The advance in human-robot interaction brings out novel applications of the disaster rescue operations. Especially, the concept of search and rescue (SAR) assisted robot operations plays an extensive role in the natural hazards, such as earthquake and wild fires. Particularly, the SAR operations in the water-based drowning due to floods and boat capsize disaster are expensive and not fast. This paper presents a survey on various SAR based remotely operated vehicle’s (ROV) related to airborne, under and surface of the water, such as unmanned marine vehicles (UMV) and unmanned aerial vehicles (UAV). In addition, the performance analysis of each UMV such as EyeROV TUNA, Saif Seas, iBubble, DTG3, Trident, Fathom One and SEAOTTER-2, is listed which helps to select the right UMV for the rescue operation at different water depths. Also discussed various SAR based UAVs like DJI Phantom-MAVIC 2, YUNEEC-H520 Hexacopter, Microdrones MD4-1000, DSLRProsMatrice 210 RTK V2 and AltiGator’sXena Drone for the flood and boat capsize operations. However, the usage of Syma X8 Pro UAV for the flood operations are worthy than Sea King SAR Chopper, which is a cost-effective operation.
A systematic literature review to address overlapping laws in Indonesia Akhyar, Amany; Saptawati, Gusti Ayu Putri
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.v14i3.8407

Abstract

The vast number of laws often result in legal uncertainty due to overlapping, conflicting, and inconsistent regulations. Identifying and resolving these overlaps is essential for ensuring legal clarity and coherence. This systematic literature review (SLR) explores technologies that have the potential to address the issue of overlapping laws in Indonesia. This study reviews numerous works on knowledge graphs (KGs) and graph mining, focusing on their potential to automate the detection of overlapping laws, thereby streamlining the process of legal harmonization. The review identifies several key research opportunities, such as refining KG construction, exploring semantic similarity measures, enhancing the interlinking of legal information, and ensuring explainability and interpretability. These opportunities promise to enhance the efficiency and effectiveness of detecting overlapping laws and contribute to a more consistent legal system in Indonesia.
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.

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