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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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Fault diagnosis of power transformer using random forest based combined classifier Prasojo, Rahman Azis; Sutjipto, Rachmat; Hanif, Muhammad Rafi; Dermawan, Chalvyn Rahmat; Kurniawan, Indra
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.v14i1.7471

Abstract

In the power system, transformers are crucial electrical equipment that require an insulator or dielectric material, such as paper immersed in insulating oil, to prevent electrical contact between components. The dissolved gas analysis (DGA) test is important for diagnosing and determining the maintenance recommendations for transformers. The duval triangle method (DTM) is commonly used to identify faults in transformers. The data used in this article are from DGA test of power transformers in East Java and Bali transmission main unit (UIT JBM). The DGA data were analyzed based on the IEEE C57.104-2019 standards, and by using the developed random forest (RF) classifier-based DTM for easier software implementation and better accuracy. The results of fault identification in 6 transformers case study showed a low-thermal fault (T1)300 °C in transformer 1, where methane gas increased, stray gassing (S) in transformer 5 due to escalating hydrogen gas production, overheating (O)≤250 °C indicated in transformers 2 and 6 due to rising ethane gas production. Transformers 3 and 4 were found in normal condition. This fault identification is done to enhance the accuracy of maintenance recommendation action based on DGA.
EXIT chart analysis of regular and irregular LDPC convolutional codes on AWGN channel Laouar, Oulfa; Amamra, Imed; Derouiche, Nadir
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.v14i1.8260

Abstract

Low-density parity-check (LDPC) codes are widely recognized for their excellent forward error correction, near-Shannon-limit performance, and support for high data rates with effective hardware parallelization. Their convolutional counterpart, LDPC convolutional codes (LDPC-CCs), offer additional advantages such as variable codeword lengths, unlimited parity-check matrices, and simpler encoding and decoding. These features make LDPC-CCs particularly suitable for practical implementations with varying channel conditions and data frame sizes. This paper investigates the performance of LDPC-CCs using the extrinsic information transfer (EXIT) chart, a graphical tool for analyzing iterative decoding. EXIT charts visualize mutual information exchange and help predict convergence behavior, estimate performance thresholds, and optimize code design. Starting with the EXIT chart principles for LDPC codes, we derived the mutual information functions for variable and check nodes in regular and irregular LDPC-CC tanner graphs. This involved adapting existing EXIT functions to the periodic parity-check matrix of LDPC-CCs. We compare regular and irregular LDPC-CC constructions, examining the impact of degree distributions and the number of periods in the parity-check matrix on convergence behavior. Our simulations show that irregular LDPC-CCs consistently outperform regular ones, and the EXIT chart analysis confirms that LDPC-CCs demonstrate superior bit error rate (BER) performance compared to equivalent LDPC block codes.
Utilizing virtual reality for real-time emotion recognition with artificial intelligence: a systematic literature review Aji Purnomo, Fendi; Arifin, Fatchul; Dwi Surjono, Herman
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.v14i1.8847

Abstract

Efficiency and optimization in virtual reality (VR) technology is an urgent need, especially in the context of optimizing algorithms to recognize user emotions while using VR. Efficient VR technology can improve user experience and enable more immersive and responsive interactions. This study adopts the preferred reporting items for systematic reviews and meta-analyses (PRISMA) (2020) method to identify and analyze gaps in the existing literature, focusing on the optimization of electroencephalogram (EEG) signal classification algorithms to recognize VR users' emotions. The literature search was conducted through the Scopus database, with article selection based on the type of emotion classified, the classification method used, the limitations of the research, and the results obtained. Of the 1478 articles found, 74 articles passed the initial selection stage, and the final stage 13 articles were selected for further analysis. The selected articles provide important insights into the development of EEG classification algorithms for VR users, especially in multi-user settings. The findings identify potential and opportunities in the development of more efficient and accurate EEG signal classification algorithms for VR users. By focusing on emotion classification in a multi-user VR environment, this research contributes to improving the efficiency of VR technology and supporting a better and more responsive user experience.
KawanSurya: an Android-based mobile app for assessing the techno-economic potential of rooftop photovoltaic Tanoto, Yusak; Marvel, Christopher; Tumbelaka, Hanny H
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.v14i1.8309

Abstract

Many developing countries, including Indonesia, are progressing poorly in residential rooftop photovoltaic (PV) adoption, including on-grid systems. On the customer side, the decision to implement on-grid rooftop PV or rely only on power from the utility grid has often been made without appropriate knowledge of techno-economic considerations. This includes the impression of high system costs. This paper introduces KawanSurya: PV calculator, a solar rooftop PV techno-economic application for Android mobile phones, designed to help residential customers assess the potential of installing on-grid rooftop PV systems. The tool allows users to select a specific geographic location, calculate daily load profiles, and determine available roof areas. It uses irradiance data from the PVGIS API and HOMER’s solar PV output equation to determine hourly PV output power. Simulation results for a typical 2,200 VA household show a payback period of 9.44 years or beyond, significantly influenced by electrical load profiles and bill reduction factors. A 65% bill reduction factor and similar load profile prolong the payback period, while a 0% billing reduction factor or uncompensated electricity sales may exceed the project’s lifetime.
Variable loaded brushless DC motor with six step commutation PID-based speed controller optimized by PSO algorithm Pangerang, Fitriaty; Arya Samman, Faizal; Zainuddin, Zahir; S. Sadjad, Rhiza
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.v14i1.8618

Abstract

This research presents a method for regulating varying voltage as a DC source in a six-step commutation brushless DC (BLDC) motor drive through control proportional integral derivative (PID) as a simple strategy for controlling the speed of BLDC motors. Strengthening the control gain uses the particle swarm optimization (PSO) algorithm by minimizing the root mean square error (RMSE) and overshoot as fitness control characteristics. The performance of the motor with the proposed controller is analyzed and compared with an experimentally-simulated-tuned PID, hybrid gray wolf optimization–proportional integral (GWO-PI), and hybrid horse herd PSO-PID (HHH PSO-PID) under changing load and speed conditions. Simulation using compose-psim altair software. Control system response parameters such as RMSE, overshoot, electromagnetic torque ripple, and phase current ripple are measured and compared with the above controllers. The results show that the proposed controller is superior to a wide range of predefined system responses.
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.

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