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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
Improved quantum inspired evolution algorithm with ResNet50 for spectrum sensing in cognitive radio networks Mochigar, Srikantha Kandhgal; Matad, Rohitha Ujjini
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.v14i1.8312

Abstract

Spectrum is considered one of the most highly regulated and limited natural resources. Cognitive radio (CR) relies on cutting-edge technology which helps to rectify the issues related to spectrum shortage in wireless communication systems. The CR technology allows the secondary user to accomplish the process related to spectrum sensing for identifying the usage of spectrum in the cognitive radio network (CRN). Though various spectrum sensing approaches are introduced, they exhibit complexity during spectrum sensing. To overcome the issues related to spectrum sensing and utilization, this research introduces improved quantum inspired evolution (IQISE) algorithm with ResNet 50 architecture. The IQISE-ResNet 50 which helps to enhance the spectrum efficiency is used in spectrum sensing. The detection of occupied and unoccupied users in CRN is performed using ResNet 50 architecture, while the IQISE is utilized in the process of training the model and optimizing the weights to enhance spectrum sensing efficiency. The experimental results show that the results achieved by the proposed approach are more effective than S-QRNN and honey badger remora optimization-based AlexNet (HBRO-based AlexNet). For example, the probability of correct classification of the proposed approach at -10 dB for binary phase shift keying (BPSK) modulation is 0.55, whereas the S-QRNN achieves an accuracy of 0.49.
Advancement in self-powered implantable medical systems Abu Owida, Hamza; Al-Nabulsi, Jamal; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; Alshdaifat, Nawaf
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.v14i1.5881

Abstract

Many different elements of patient monitoring and treatment can be supported by implantable devices, which have proven to be extremely reliable and efficient in the medical profession. Medical professionals can use the data they collect to better diagnose and treat patients as a result. The devices’ power sources, on the other hand, are battery-based, which introduces a slew of issues. As part of this review, we explore the use of harvesters in implanted devices and evaluate various materials and procedures and look at how new and improved circuits can enable the harvesters to sustain medical devices.
Expert judgment, limitation inference, and threshold values to optimize diagnosis in eye diseases expert system Wahyudi Oktavia Gama, Adie; Gede Hendra Divayana, Dewa; Gusti Ngurah Darma Paramartha, I; Made Widnyani, Ni
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.v14i1.8204

Abstract

This research aimed to develop an optimal expert system by adopting a simplified approach. The methodology integrates an expert judgment approach, limitation inference, and establishing a threshold value. Expert judgment is pivotal in assigning a percentage weight to each rule, facilitating a nuanced evaluation of diagnostic criteria to augment the system's precision. Moreover, incorporating limitation inference strategically constrains the number of user inquiries, streamlining the diagnostic process and enhancing overall efficiency. Additionally, the imposition of a threshold value ensures a more precise early diagnosis by delineating specific criteria for condition identification. This comprehensive approach underscores the paramount importance of user experience and aims to alleviate the burden on individuals seeking a diagnosis. Ultimately, the anticipated outcome of this study is the development of an expert system poised to deliver early diagnoses with heightened efficiency and accuracy. By integrating expert judgment, limitation inference, and threshold values, this research embodies a refined and user-centric paradigm for eye disease diagnosis, promising significant advancements in global eye health.
Modeling 6(10)-35 kV electrical network for fault location via negative correlation Saken Koyshybaevich, Sheryazov; Anastasia Igorevna, Uspanova; Titko, Jelena; Igor Vladimirovich, Koshkin; Arman Bolatbekovich, Utegulov
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.v14i1.7544

Abstract

In order to maintain the technical leadership of the economic sector in any nation, there is currently a greater focus on guaranteeing the fail-safe operation of electrical networks and electrical equipment. This paper presents a model for evaluating the fault location procedure based on computer simulation in MATLAB/Simulink of complex 6(10)-35 kV power line systems. The proposed algorithm for preprocessing electrical network signals in normal and emergency modes uses a negative statistical correlation of all possible electrical parameters, while the resulting percentage errors when estimating the location of the fault are within acceptable limits. Algorithms and significant parameters have been determined for effectively carrying out the procedure for searching for the location of a fault through the use of modeling programs, namely: zero-sequence voltage, negative-sequence voltage, initial current value. and the positive sequence voltage is the transition resistance at the accident site. An assessment of the results of preliminary modeling may indicate that devices for finding the location of a fault in the 6(10)-35 kV electrical network will be able to use information obtained about the object using the developed methodology, adjust calculation algorithms and take into account the operating modes of the electrical network.
Deep learning approaches for analyzing and controlling rumor spread in social networks using graph neural networks Manurung, Jonson; Sihombing, Poltak; Andri Budiman, Mohammad; Sawaluddin, Sawaluddin
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.v14i1.8143

Abstract

The pervasive influence of social networks on information dissemination necessitates robust strategies for understanding and mitigating the spread of rumors within these interconnected ecosystems. This research endeavors to address this imperative through the application of a graph neural network (GNN) model, designed to capture intricate relationships among users and content in social networks. The study integrates user-level attributes, content characteristics, and network structures to develop a comprehensive model capable of predicting the likelihood of rumor propagation. The proposed model is situated within a broader conceptual framework that incorporates sociological theories on information diffusion, user behavior, and network dynamics. The results of this research offer insights into the interpretability of the GNN model’s predictions and lay the groundwork for future investigations. The iterative refinement of the model, consideration of ethical implications, and comparison against traditional machine learning baselines emerge as crucial steps in advancing the understanding and application of deep learning methodologies for rumor control in social networks. By embracing the complexities of real-world scenarios and adhering to ethical standards, this research strives to contribute to the development of proactive tools for rumor management, fostering resilient and trustworthy online information ecosystems.
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.

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