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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 1: February 2025" : 75 Documents clear
Design and optimization of a linear fiber-reinforced soft actuator for improved linear motion performance Md Ghazaly, Mariam; Yee Wong, Min; Abdullah, Zulkeflee; Hasim, Norhaslinda; Maisarah Mohd Sobran, Nur; Izzuan Jaafar, Hazriq; Zainal, Nasharuddin
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8557

Abstract

The demand for safe and flexible actuators has increased as traditional actuators pose safety risks due to their rigid materials, especially in applications requiring human-machine interaction. This study focuses on designing and optimizing a linear fiber-reinforced soft actuator to enhance linear motion performance while maintaining safety and flexibility. Finite element method (FEM) analysis was used to evaluate the effects of varying key design parameters, including core radius, actuator length, and core wall thickness. The analysis revealed that increasing the core radius leads to greater linear extension, while increasing the actuator’s length and wall thickness reduces extension. Among the tested designs, the R10 design exhibited the highest linear extension, with a 44.41% increase in length compared to the original design. However, the R10 design also showed undesirable bulging at the free end under pressure, which necessitated further optimization. By increasing the thickness of the sheath wall, the bulging was reduced, and the optimized design achieved a 34.53% increase in extension. This study highlights the significance of parameter optimization in fiber-reinforced soft actuators to achieve superior linear motion performance. Future work will explore further improvements in structural stability, sensor integration for precise control, and advanced fabrication techniques for better customization and durability.
Analysis of human emotions through speech using deep learning fusion technique for Industry 5.0 Anil Kumar, Chevella; Sagar Reddy, Vumanthala; Pravallika, Ambati; Y. Chalapathi, Rao; Syamala, Neelam
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8464

Abstract

Emotions are important for human well-being and social connections. This work focuses on the issue of effectively understanding emotions in human speech, specifically in the context of Industry 5.0. Traditional approaches and machine learning (ML) techniques for identifying emotions in speech are limited, such as the requirement for complicated feature extraction. Traditional methods yield recognition accuracies of no more than 90% because to the restricted extraction of temporal/sequence information. This paper suggests a ground-breaking fusion-based deep learning (DL) method to overcome these limitations. Specifically, one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) can automatically extract significant characteristics and handle enormous datasets in real time. Furthermore, a fusion-based DL network, speech emotion recognition deep learning fusion network (SER_DLFNet), has been proposed, which combines CNN with long short-term memory (LSTM) to collect sequence information and increase recognition accuracy. The proposed model shows impressive results, with a test accuracy of 95.52% on the ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. This research contributes to the advancement of more precise and efficient emotion identification algorithms for voice analysis, especially within the framework of Industry 5.0.
Analysis of alternatives methodology for large-scale information system implementation Arisal, Andria; Setiadi, Bambang; Muslim, Ichwanul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7800

Abstract

According to the Presidential Decree, central and local governments must implement electronic-based government systems or sistem pemerintahan berbasis elektronik (SPBE). However, the independent implementations have created various similar applications to support the same field of governmental activities. The situation creates difficulties in achieving effectiveness, integration, sustainability, efficiency, accountability, interoperability, and security of governmental services. Therefore, a common application will be developed for each governmental activity to improve interoperability and data integration. On the other hand, central or local governments must consider the suitable implementation of their public service information systems. This manuscript guides the determination of alternatives using cost, benefit, and risk analysis. We use the proposed guidance for a case study because sistem pengelolaan pengaduan pelayanan publik nasional-layanan aspirasi dan pengaduan online rakyat (SP4N-LAPOR!) has been regulated as the common application for Public Service Complaints Management using PermenPANRB No. 680, 2020. The application of the proposed guidance shows that it can help the stakeholder quantitatively decide on an alternative implementation of the application for the public service complaints management system.
An overview of 33 years of trends in space weather research: a bibliometric analysis (1988-2021) Asraf Hairuddin, Muhammad; Zainuddin, Aznilinda; Iffah Abd Latiff, Zatul; Mohd Anuar, Nornabilah; Dalila Khirul Ashar, Nur; Sharizat Hamidi, Zety; Hassan Nordin, Abu; Ihsan Mohd Yassin, Ahmad; Yoshikawa, Akimasa; Huzaimy Jusoh, Mohamad
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8159

Abstract

Space weather (SpW) is a phenomenon caused by a variety of solar events and has the potential to disrupt infrastructure systems and technology, putting them at risk. Despite SpW’s immense impact, there has been a notable absence of bibliometric analysis studies to understand the research trends, regional distribution, social structure, conceptual structure, and knowledge gaps. This review synthesized scopus documents of SpW domain from 1988 to 2021. In this study, three tools were used, such as Microsoft Excel, VOSviewer, and Harzing’s Publish or Perish for statistical analysis, graphical presentation, and citation metrics, respectively. Based on the 3,956 articles, roughly 70% of the articles were published in the last ten years, reveals a rapid growth in SpW research. The study discovered that China ranked third in publication volume, following the United States and the United Kingdom with Russian Federation following closely in fourth place. This study also presents six key findings, including the growth pattern of publications, contributions, and authorship collaboration by countries, most productive and influenced authors, co-authorship status, most influenced journals and articles, research cluster and new SpW subtopics discovered. These findings provide useful insight and aid in the advancement and progress of this field.
Design type-2 fuzzy for superconducting magnetic energy storage to enhance frequency transient response Abdillah, Muhammad; Bagus Laksono, Arie; Indriani Pertiwi, Nita; Aryo Nugroho, Teguh; Setiadi, Herlambang; Araminta Jasmine, Senit; Evanda Putra, Naufal
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.6654

Abstract

Renewable energy has become a new trend in power systems. Renewable-based power plants such as wind power systems and photovoltaics. This paper proposed a novel method for inertia emulation based on superconducting magnetic energy storage (SMES). To get better inertia support for the system, a type-2 fuzzy controller is used as the SMES controller. An area power system is used as the test system to investigate the performance of type-2 fuzzy controller on SMES. Time domain simulation is carried out to show the efficacy of the proposed method. From the simulation results, it is found that the proposed controller can reduce the overshoot of frequency by up to 20% compared to the type-1 fuzzy controller. It is also hoped that the proposed method can be used as a reference of the Industrial people.
DEMNET NeuroDeep: Alzheimer detection using electroencephalogram and deep learning M. Joshi, Vaishali; P. Dandavate, Prajkta; Rashmi, R.; R. Shinde, Gitanjali; D. Kulkarni, Deepthi; Mirajkar, Riddhi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8163

Abstract

Alzheimer’s disease (AD) stands out as the most prevalent neurological brain disorder, and its diagnosis relies on various laboratory techniques. The electroencephalogram (EEG) emerges as a valuable tool for identifying AD, offering a quick, cost-effective, and readily accessible means of detecting early-stage dementia. Detecting AD in its early stages is crucial, as early intervention yields more successful outcomes and entails fewer risks than treating the disease at a later stage. The objective of this research is to create an advanced diagnosis system for AD using machine learning (ML) and EEG data. The proposed system utilizes a multilayer perceptron (MLP) and a deep neural network with bidirectional long short-term memory (BiLSTM) as the classifier. The feature extraction process involves incorporating Hjorth parameters, power spectral density (PSD), differential asymmetry (DASM), and differential entropy (DE). The BiLSTM classifier, particularly when combined with DE, exhibits outstanding performance with an accuracy of 97.27%. This amalgamation of DE and the deep neural network surpasses current state-of-the-art techniques, underscoring the substantial potential of this approach for precise and advanced diagnosis of AD.
Enhancing detection of zero-day phishing email attacks in the Indonesian language using deep learning algorithms Roesmiatun Purnamadewi, Yasinta; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8759

Abstract

Email phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patterns that differ from those in other language contexts. Four models are tested, combining Indonesian bidirectional encoder representation of transformers (IndoBERT) and FastText feature extraction techniques with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning algorithms. The results indicate that the combination of FastText and CNN achieved the highest performance in accuracy, precision, and F1-score metrics, each at 98.4375%. Meanwhile, the FastText model with LSTM showed the best performance in recall, with a score of 98.9583%. The research suggests exploring deeper into email content or integrating analysis between headers and email content in future studies to further improve accuracy and effectiveness in phishing email detection.
Identification and validation of factors affecting the success of smart village services Hadian, Nur; Purwanto, Purwanto; Wibowo, Adi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8022

Abstract

This study aims to explore and validate the factors that influence the performance and effectiveness of smart village services. Smart villages have become a focus for improving the quality of life of rural communities in the era of digital technology. However, there is a lack of methods to measure and evaluate the effectiveness of smart villages. We propose a holistic framework to measure and evaluate the effectiveness of smart services in smart villages. In this study, factors that influence the success of smart village effectiveness are identified. How effective the smart village services are can be understood using the information system success model approach by DeLone and McLean. This framework is expected to provide a better understanding of the effectiveness of smart village services so that people are willing to adopt the smart village service concept. In addition, this model can also be used as decision-making support for stakeholders and is expected to improve the quality of life of rural communities in a sustainable manner.
Global research trends in building-integrated photovoltaics: a bibliometric analysis (1971-2022) Tambunan, Handrea Bernando; Digwijaya, Wigas; Nurfanani, Achmad
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7453

Abstract

The number of academic publications in the building-integrated photovoltaics (BIPV) field has rapidly grown. Most published articles focus on a specific topic, such as mathematical model, solar architecture design, photovoltaic effect, solar cell, grid-connected, efficiency, performance assessment, economic analysis, optimization, and others with broader focus areas. This work focuses on BIPV research with bibliometric analysis through documents, cited references, authors, affiliations, countries, funding sponsors, sources, words, and conceptual structure based on the Scopus-indexed database between 1971 and 2022. The result shows that BIPV research constantly grows annually with strong collaboration authorship. China is the most relevant country with the top affiliation and funding sponsor to support the BIPV research. The terms conjugated polymers, photovoltaic properties, and organic polymer are identified as niche themes. On the other hand, the terms of conversion efficiency, perovskite, photovoltaic devices, solar cells, efficiency, and photoelectrochemical cell clusters are emerging themes. In the future, BIPV research will move towards microgrids, energy, performance, energy management systems, and energy efficiency issues. The finding will also provide researchers and organizations with a comprehensive understanding of BIPV research areas and new directions for future research.
Implementation of radio frequency identification technology for a secure and intelligent shopping cart N. Arinze, Stella; U. Okafor, Patrick; R. Obi, Emenike; O. Nwajana, Augustine
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8243

Abstract

Shopping at supermarkets has become a daily activity in urban areas of Enugu, Nigeria. However, there is always a huge rush in most mega supermarkets during times of discount offers, weekends and holidays resulting in long queues due to the barcode billing process. This research proposes a way of reducing the time spent at the billing counter using a radio frequency identification (RFID) smart-based shopping cart. To achieve this objective, an RFID tag, RFID reader, Arduino microcontroller and light-emitting diode (LED) display were used to develop a smart shopping trolley. RFID tag was placed on each of the eight products displayed for sale. RFID reader reads all the products that were placed on the cart and the details of the product such as the name, quantity, cost, and total cost was displayed on the LED. The smart shopping trolley system also incorporates an alarm system that triggers off when the RFID tag is removed from a product to avoid shoplifting and make the system secure for the owners of the supermarket. The result showed that the billing of the products was done directly from the smart shopping cart. The system was compared with the conventional barcode system and was found to overcome the limitations of time-consuming billing procedures.

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