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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 73 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 73 Documents clear
Performance of an internet of things based plant monitoring and irrigation system using solar energy Affzani Md Rozani, Md Azim; Azha Mohd Annuar, Khalil; Razali Mohamad Sapiee, Mohd; Kumar Debnath, Sanjoy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9085

Abstract

This paper aims to present an internet of things (IoT) based plant monitoring and irrigation system powered by solar energy. The system enhances plant care by continuously monitoring environmental conditions such as soil moisture, temperature and humidity with real data displaye via the Blynk platform. User can remotely monitor and control irrigation through interactive widgets, ensuring efficient plant management. By integrating solar energy, the system operates sustainably, and reduce reliance on conventional electricity. Performance evaluation demonstrates a temperature sensor accuracy of 98%, a humidity sensor accuracy of 95% and soil moisture sensor error margin of 2-3%. Experiment results indicate improved plant growth of 7-8% compared to traditional farming practices, showcasing the system’s potential for increased productivity and conversion. This research highlight the benefits of combining IoT and renewable energy to offer an innovative, and eco-friendly solution for agricultural management.
Application of two inductors with single magnetic core in a two-level current source inverter Suroso, Suroso; Prasetijo, Hari
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9578

Abstract

Current source inverter (CSI) transforms DC current into a predetermined AC current. In practice, the DC current are acquired by connecting inductors with the DC power source. Common-emitter current source inverter (CE-CSI) is an inverter where the emitter terminals of the insulated gate bipolar transistors (IGBTs) or metal oxide semiconductor field effect transistors (MOSFETs) switches are connected at a common voltage. This inverter requires two non-isolated DC current sources as input power. The two level CE-CSI is the simplest circuit of the CE-CSIs. The circuit was able in simplifying inverter circuits compared to the three-level CE-CSI in case of device number, i.e., diodes, IGBTs/MOSFETs, and gate drive circuits. This paper studied the basic characteristics of the two-level CE-CSI when two reactors with a single magnetic core were used. The inverter circuit was examined and evaluated through computer tests, and experimentally. The two-level CE-CSI was able to generate a low distortion of sinusoidal AC load current with total harmonic distortion (THD) value 1.92%. Test data showed that the magnitudes of low order harmonics were less than 0.3% of the fundamental frequency. Moreover, the inverter efficiency can be increased due to reduction of the power losses caused by power switching devices.
Incremental learning based fuzzy reasoning approach for diagnosis of thyroid disease Ramanath, Thirumalaimuthu Thirumalaiappan; Hossen, Md. Jakir
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9140

Abstract

This paper presents a novel hybrid fuzzy logic approach for the classification of thyroid disease. Hybrid fuzzy logic approaches have brought many benefits to the medical data classification problems such as reasoning on uncertain or incomplete data. The machine learning algorithms had been used with the fuzzy expert systems to define the fuzzy rule base. The optimization techniques had been used in the fuzzy expert systems for optimizing the fuzzy membership functions and fuzzy rules. Enhancing the machine learning algorithms and optimization techniques that are integrated with the fuzzy logic method can improve the overall performance of the fuzzy expert system. To deal with the curse of dimensionality problem and to enhance the integration of machine learning algorithm and fuzzy logic method, this paper presents an incremental learning based parallel fuzzy reasoning system (IL-PFRS) for medical diagnosis. In this research work, the decision tree classifier is used to extract the features from dataset. IL-PFRS is applied to classify the thyroid disease which is serious disease that needs attention and earlier detection. The thyroid disease dataset obtained from the UCI machine learning repository is used in this research work where the IL-PFRS showed the classification accuracy of 99% when testing using this dataset.
Torque control of PMSM motors using reinforcement learning agent algorithm for electric vehicle application Ha, Vo Thanh; Tuan, Duong Anh; Van, Tran Thuy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7852

Abstract

As electric vehicles (EVs) demand higher performance and efficiency, precise torque control in interior permanent magnet synchronous motors (IPMSMs) becomes increasingly vital. This paper introduces a reinforcement learning (RL)-based method to optimize torque control in IPMSMs. The RL agent is trained to regulate d-axis and q-axis currents, producing stator voltages to follow the desired motor speed. The control system includes an observation vector, voltage-based actions, and a specially designed reward function. Due to the nonlinear dynamics of the motor, training the agent requires significant computational effort. MATLAB/Simulink simulations are performed to compare the RL controller with a traditional PI controller. Results indicate that the RL controller delivers quicker and more accurate performance, although additional training is necessary to minimize overshoot.
HBRFE: an enhanced recursive feature elimination model for big data classification Varadharajan, Kesavan Mettur; Kumar, Josephine Prem; Ashwin, Nanda
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9595

Abstract

The process of classification in big data is a tedious task due to the large number of volumes, veracity, and variety of the data. Classification of big data pave the path to organize the data and improve the classifier performance. This research article proposed a Hadoop framework based recursive feature elimination-based model called HBFRE for extract significant features from the big data by integrating map and reduce frame work. HBFRE extract the significant features by removing the least and irrelevant features from the dataset by using refined recursive feature elimination (RFE) with map and reduce framework. This method takes the mean of each attribute and find the variance in each instance. The proposed model is evaluated and analyzed by the accuracy performance and time complexity. This research utilized various classifier like artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), and AdaBoost to measure the classification performance on the big data. Proposed HBRFE model is compared with different feature selection like RFE, relief, backwards feature elimination, maximum relevance k-nearest neighbors (MR-KNN), and scalable deep ensemble framework big data classification (SDELF-BDC).
Advance technique for online condition monitoring of surge arresters Khopkar, Anil S.; Pandya, Kartik S.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9457

Abstract

Gapless surge arresters (GLSA) constructed with zinc oxide (ZnO) element is connected in electrical power system for protection against surge voltage. For condition monitoring of GLSA conventionally offline and online techniques are available. However, offline techniques are not much useful as it requires system shutdown and hence online techniques are more useful. Online surge arrester monitoring technique based on leakage current analysis is adopted by all stakeholders. However, in this method supply system harmonics plays major roles in measurement accuracy to determine health index. In this paper improved health monitoring indexes for GLSA diagnostic based on ratios of leakage current components has been proposed. The ageing process has been done with application of lightning Impulse current, surface contamination due to salt fog, temperature effect, and moisture ingress. Various experimental tests have been carried out on porcelain and polymer housed surge arresters to evaluate the ability of proposed method. Obtained results of 9 kV, 18 kV, and 30 kV healthy and degraded metal oxide GLSAs have been shown the viability of improved health indexes on surge arrester condition monitoring procedures. The experimental investigation and discussion of the obtained results reflects sufficient and effective trails to utility engineers to determine health of surge arresters and able to effectively schedule further maintenance plan.
PMU-data assisted state estimation of distribution network with integrated renewables: a comprehensive review Khanam, Nida; Rihan, Mohd.; Hameed, Salman
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9019

Abstract

The variability and distributed nature of renewable energy sources (RES) pose challenges to real-time monitoring and control in distribution networks. Phasor measurement units (PMUs) provide high-precision, time-synchronized measurements, significantly improving state estimation (SE) accuracy in complex grids. This paper reviews SE in distribution systems using PMU data, focusing on challenges introduced by high-RES integration. Traditional techniques, such as weighted least squares (WLS), are analyzed, revealing limitations like reduced observability and accuracy due to RES intermittency. To address these challenges, advanced methods such as robust optimization, dynamic network reconfiguration, and decentralized control are explored, showing improved network reliability and adaptability under RES variability. Furthermore, innovative approaches like Bayesian non-parametric modelling are discussed, offering solutions to mitigate uncertainties and enhance grid flexibility. Case studies highlight the scalability and effectiveness of PMUs in extensive networks, showcasing their role in improving both SE precision and system stability. These findings underline the critical need for precise and integrated SE techniques to develop resilient, adaptable smart grids capable of accommodating the increasing penetration of RES, setting a foundation for future technological advancements.
Screening capabilities for the 3D dyscalculia identification game framework Pudjoatmodjo, Bambang; Salam, Sazilah; Naim Che Pee, Ahmad; Aherliwan Rudavan, Rikman; Setijadi Prihatmanto, Ary
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8765

Abstract

Dyscalculia, a learning difficulty in mathematics, remains a concealed challenge affecting individuals of average intelligence or remarkable creativity. This inconspicuous disability often leads teacher to misinterpret students as lacking intellect. Regrettably, this condition can prompt students to disengage from routine activities, resulting in diminished performance and self-confidence. To address this issue, our research introduces a serious game framework, namely the “3D-dyscalculia identification game framework” (3D-DIG framework), integrating a screening feature aimed at detecting mathematical shortcomings in students. This paper focuses on detailing the screening feature, wherein a Petri net structure orchestrates its functionality within the 3D game environment. Specifically, our study highlights how this feature assesses and captures potential student deficiencies during work on game challenges. Employing game engine, and web server technologies, the dyscalculia screening feature captures students' responses, enabling an evaluation of their mathematical proficiency. Analysis of student data affirms that the screening feature's in identifying potential mathematics-related deficiencies. Moreover, the 3D game incorporates a distinctive element: it notifies teachers when a student surpasses a 60-second threshold while solving a problem, facilitating timely interventions. By offering actionable insights, the framework empowers teacher to identify student with the mathematics' deficiency and support the student with the appropriate intervention.
A transformer-based time series forecasting model with an efficient data preprocessing scheme Yemets, Kyrylo; Gregus, Michal
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9280

Abstract

Time series forecasting with cyclicality is key to the development of green energy, particularly wind energy, due to its high volatility. Accurate forecasting allows for optimal use of energy storage systems and balancing of power grids. In this article, the authors have developed a model for forecasting time series in wind energy through the combined use of Fourier transform and an adapted transformer architecture to solve the time series forecasting problem. The use of Fourier transform provided the ability to detect and account for hidden periodicities that may not be obvious in simple time series analysis, and allowed for the separation of random fluctuations from significant cyclical components, contributing to more accurate data analysis. The use of transformer architecture made it possible to effectively account for both short-term fluctuations and long-term trends in wind patterns, creating more accurate and reliable forecasts of wind energy production. The results show that the model outperforms methods such as transformers, long short term memory (LSTM), LSTM with Fourier transform, and DeepAR in forecast accuracy, taking into account seasonal, weather, and daily cycles of wind data.
Improved non-invasive diagnosis of hepatocellular carcinoma by optimized meta classifier with hybridized features Thamby, Babitha; Jayakaran Thomson Fredrik, Edwin
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9412

Abstract

Hepatocellular carcinoma (HCC), the primary cancer of the liver, is life-threatening, with few or no symptoms, and detection in the early stage will help for successful treatment with surgery, and transplant for a better life quality. Here, we proposed two stacking classification models based on deep learning with differential hybrid feature selection for the early detection of HCC using novel non-invasive biomarker PIVKA-II. We showed how the variations in hybrid feature selection affect the performance of stacking classification and different supervised machine-learning algorithms on a metaclassifier. The base layers were support vector machine (SVM), gradient boosting (GB), and linear discriminant analysis (LDA). The meta classifier was a multilayer perceptron (MLP) with three different optimizers, stochastic gradient descent (SGD), adaptive moment estimation (ADAM), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Our first model outperformed the second with their hybrid features by improving accuracy by 1.5% and F1_score by 1% in both SGD and ADAM optimization, while MLP-LBFGS had a 1.4% increase in accuracy. The precision had hiked by 1.9%, 3.5%, and 1.7% in SGD, ADAM, and LBFGS, respectively, in Model-1. Matthew’s correlation coefficient (MCC) for MLP-SGD increased from 0.82 to 0.85, MLP-ADAM from 0.81 to 0.85, and MLP-LBFGS from 0.75 to 78 for the first model.

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