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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 2: April 2025" : 75 Documents clear
POA-DT: a novel method for predicting air quality in major Indian cities Megavarnan, Gayathri; Venkatachalam, Kavitha
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8958

Abstract

Air pollution is a critical environmental and public health concern, exacerbated by urbanization, industrial growth, and increased transportation. The air quality index (AQI) in major cities is significantly elevated due to rapid industrial expansion, fossil fuel consumption, and vehicular emissions. This study aims to predict AQIs using machine learning techniques, specifically integrating the Pelican optimization algorithm (POA) with the decision tree (DT) method to enhance accuracy. Data from prominent Indian cities—Mumbai, Delhi, Bangalore, Kolkata, and Chennai—was analyzed due to their high pollution levels. The model’s performance was validated against traditional machine learning methods such as k-nearest neighbors (KNN), random forest (RF) regression, and support vector regression (SVR). Results showed the highest prediction accuracies for Kolkata at 96.68%, followed by Bangalore at 95.66%, Chennai at 93.10%, Mumbai at 92.48%, and Delhi at 86.61%. These findings demonstrate that the proposed model outperforms conventional techniques in predicting AQI, providing a foundation for effective policy-making to mitigate air pollution impacts.
Design and implementation of an industrial security system using color cameras Ouhoud, Amina; Hakim Guezzen, Amine
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8431

Abstract

This paper examines the design, development, and implementation of a modern industrial security system that integrates color cameras to enhance surveillance and improve safety. The system leverages cutting-edge technologies to detect intrusions and incidents with greater accuracy, which significantly strengthens the security of industrial sites. The study focuses on key stages of the project, including the design, installation, and operational processes, while addressing the challenges encountered during these phases. The integration of color cameras provides clearer and more precise monitoring, allowing for quicker detection and response to potential threats, thus reducing risks effectively. Our results demonstrate that the system greatly improves surveillance efficiency, providing a reliable and robust solution tailored to the security demands of industrial environments. This research offers in-depth insights into the system’s design and functionality, showcasing its critical role in safeguarding industrial facilities. Overall, the proposed solution is an essential tool for enhancing safety protocols and risk management strategies, contributing to more secure industrial sites.
Optimizing the best student selection: hybrid K-Means approach and entropy-grey relational analysis Sulistiani, Heni; Setiawansyah, Setiawansyah; Palupiningsih, Pritasari; Ferico Octaviansyah Pasaribu, Ahmad; Andika, Rio; Hamdan Sobirin, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8715

Abstract

The selection of the best students is an important process in recognizing students' achievements and dedication in various fields. Through careful and fair selection, students who stand out in both academic and non-academic terms can be identified and assigned. The purpose of the research on the use of hybrid entropy-grey relational analysis (GRA) and K-Means clustering in the selection of the best students is to develop a more objective, accurate, and comprehensive assessment system. The silhouette score results show that 2 clusters have a value of 0.5733, so in this study 2 clusters are used with the best cluster at cluster 0. Data from cluster 0 will be used in determining the best students using hybrid entropy-GRA. The results of the best student ranking using the hybrid entropy-GRA method, for the first best student with a final score of 0.25 were obtained by Mareta Amelia. The hybrid approach of K-Means and entropy-GRA offers a powerful tool to improve decision-making in the student selection process. The hybrid approach of K-Means grouping and entropy-GRA presents a powerful solution, improving the decision-making process and ensuring that high-achieving students are accurately recognized and rewarded.
Environmental odor detection and classification with electronic nose system Macías-Quijas, Ricardo; Velázquez, Ramiro; Del-Valle-Soto, Carolina; Lizut, Rafal; Visconti, Paolo; Lay-Ekuakille, Aimé
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9046

Abstract

A prototype of an electronic nose (e-nose) system integrating a set of general-purpose gas sensors, an electronic module, and signal processing and classification methods has been designed and implemented to detect certain environmental odors that might pose a risk to human health. The proposed device explores the filter diagonalization method (FDM), an advanced signal processing technique for accurate spectral estimation, to detect the presence of odors together with random forest (RF), a popular machine learning algorithm, to classify the features of such spectra. Experimental results show that the proposed FDM-RF approach can recognize the targeted odors with an accuracy of 96.4%.
Development of distance formulation for high-dimensional data visualization in multidimensional scaling Marto Hasugian, Paska; Mawengkang, Herman; Sihombing, Poltak; Efendi, Syahril
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8738

Abstract

This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Solving problems of the flexible scheduling machines Geci, Fis; Bytyçi, Eliot
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.6195

Abstract

Flexible job scheduling problem (JSP) as an optimization problem, tends to find solution for allowing different operations to be processed faster. This problem could be solved by genetic algorithm, as we have proven in another experiment. Now, we have tried to outperform state of the art, by using parallel genetic algorithm. Parallel genetic algorithm has two types and we have chosen the most popular one coarsed grained genetic algorithm, for our specific case. The results have improved time wise and are promising in some of the datasets, while a need exists for improving on other ones. In the future, we will compare both versions of parallel genetic algorithms but also compare the results to another algorithm.
Graphene based nano-antenna for wireless communication systems at terahertz band Singh, Raj Kumar; Mamta, Kumari; Kumar, Dhirendra
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8727

Abstract

The need for nano-antennas with decreased size and the capacity to operate at mid-infrared frequencies to enable adequate coverage of signal is being investigated. In this paper, we present graphene-based nano-antenna and analysed at the resonating frequency 33 THz using gallium arsenide material as a substrate having dielectric constant 11.35 and a loss tangent of 5.6×10-4 for terahertz (THz) frequency. The height of substrate is optimized to 108 nm and in-plane dimension being 1,700×1,400 nm. Graphene was used as a rectangular patch with dimension 850×450×5 nm and ground having chemical potential=1.4 eV, and relaxation time=1 ps, to achieve high gain and bandwidth. Impact of slot width variation on the antenna parameters have been reported in terms of reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern and gain. Reported beam width being 90.4° for both electric and magnetic planes. Proposed antenna achieved a return loss of -18.38 dB, VSWR less than 2, indicating good match with load, highest gain of 8.8 dBi and bandwidth of 500 GHz at the target resonance frequency making it suitable for 5G/6G mm wave wireless communication.
The impact of BERT-infused deep learning models on sentiment analysis accuracy in financial news Ndama, Oussama; Bensassi, Ismail; Mokhtar En-Naimi, El
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8469

Abstract

This study delves into the enhancement of sentiment analysis accuracy within the financial news domain through the integration of bidirectional encoder representations from transformers (BERT) with traditional deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM) networks, gated recurrent units (GRU), and convolutional neural networks (CNN). By employing a comprehensive method encompassing data preprocessing, polarity analysis, and the application of advanced neural network architectures, we investigate the impact of incorporating BERT’s contextual embeddings on the models’ sentiment classification performance. The findings reveal significant improvements in model accuracy, precision, recall, and F1 scores when BERT is integrated, surpassing both traditional sentiment analysis models and contemporary natural language processing (NLP) transformers. This research contributes to the body of knowledge in financial sentiment analysis by demonstrating the potential of combining deep learning and NLP technologies to achieve a more nuanced understanding of financial news sentiment. The study’s insights advocate for a shift towards sophisticated, context-aware models, highlighting the pivotal role of transformer-based techniques in advancing the field.
Hybrid algorithm for optimized clustering and load balancing using deep Q reccurent neural networks in cloud computing Vijay Kumar, Nampally; Mohanty, Satarupa; Kumar Pattnaik, Prasant
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9123

Abstract

Cloud services are among the technologies that are developing the fastest. Additionally, it is acknowledged that load balancing poses a major obstacle to reaching energy efficiency. Distributing the load among several resources in order to provide the best possible services is the main purpose of load balancing. The network's accessibility and dependability are increased through the usage of fault tolerance. An approach for hybrid deep learning (DL)-based load balancing is proposed in this paper. Tasks are first distributed in a round-robin fashion to every virtual machine. When assessing whether a virtual machine (VM) is overloaded or underloaded, the deep embedding cluster (DEC) also considers the central processing unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors. For cloud load balancing, the tasks completed on the overloaded VM are assigned to the underloaded VM based on their value. To balance the load depending on many aspects like supply, demand, capacity, load, resource utilization, and fault tolerance, the deep Q recurrent neural network (DQRNN) is also suggested. Additionally, load, capacity, resource consumption, and success rate are used to evaluate the efficacy of this approach; optimum values of 0.147, 0.726, 0.527, and 0.895 are attained.
Strategic processor task allocation through game-theoretic modeling in distributed computing environments Telmanov, Merlan; Suchkov, Mikhail; Abdiakhmetova, Zukhra; Kartbayev, Amandyk
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9257

Abstract

This paper explores a game-theoretic model for task allocation in distributed systems, where processors with varying speeds and external load factors are considered strategic players. The goal is to understand the impact of processors' strategic behaviors on workload management and overall system efficiency, focusing on the attainment of a pure strategy Nash Equilibrium (NE). The research rigorously develops a formal mathematical model and validates it through extensive simulations, highlighting how NE ensures stability but may not always yield optimal system performance. The adaptive algorithms for dynamic task allocation are proposed to enhance efficiency in real-time processing environments. Results demonstrate that while NE provides stability, the adoption of optimal cooperative strategies significantly improves operational efficiency and reduces transaction costs. The findings contribute valuable insights into the strategic interactions within computational frameworks, offering guidelines for developing more efficient systems. This study not only advances the theoretical understanding of strategic task allocation but also has practical implications for system design and policy-making in areas such as cloud computing and traffic management.

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