cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 281   282   283   284   285 
Deflection enhancement of ferrite magnetic core-based microactuator Pawinanto, Roer Eka; Mulyanti, Budi; Fauzan, Jahril Nur; Subandi, Ayub; Hasanah, Lilik; Pangestu, M. Assadillah; Yunas, Jumril
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.v14i2.8448

Abstract

Microactuators play a vital role in several microelectromechanical systems (MEMS) that generate forces or deflections necessary to accomplish functions such as scanning, tuning, manipulation, or delivery. Utilizing a ferrite magnetic core has shown the potential to enhance the deflection of the microactuator. However, the previous study presented a complex fabrication method with high power consumption unsuitable for micropump application. Herewith, we report the impact of ferrite core length on the deflection generated by a microactuator with a simple fabrication method. The deflection behavior shows that the corresponding magnetic core length is inverse to the deflection improvement. The force reduction generated led by a longer magnetic core because of the farther distance to the coil. Our study can be used as a reference to support the development of micropump or active micromixer devices, which require compact devices with simple fabrication and high deflection, achieving ultra-high flow rate and high mixing index.
Efficient diabetic retinopathy detection using deep learning approaches and Raspberry Pi 4 Ajith Kumar, Silpa; Kumar, James Satheesh; Bharadwaj, Sharath Chandra
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.v14i2.8248

Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss, predominantly affecting individuals aged 25-74 with diabetes mellitus. Timely medical intervention can protect against irreversible blindness in over 90% of cases, emphasizing effectively identifying and treating DR. In the scope of deep learning (DL), the possibility of using them in DR screening has garnered a lot of interest. Specifically, we adopted the densely connected convolutional networks (DenseNet) model because to its capacity to acquire complex features and learn from diverse datasets. Developing the computational model on retinal images labelled with varying phases of DR are obtained from databases such as Messidor and Kaggle. To enhance accessibility and user-friendliness, we integrated the DenseNet model into a Raspberry Pi 4, a compact, affordable and widely accessible computing platform. The proposed approach resulted in an impressive classification accuracy of 88%, demonstrating its proficiency in distinguishing between different phases of DR progression. The study aims to assist in the early detection and diagnosis of the disease, providing a potential resource that could help medical practitioners and ophthalmologists to evaluate the extent of DR in a timely manner.
Developing digital capabilities through IT governance: a PLS-SEM analysis in Moroccan higher education institutions Chahid, Abdelilah; Ahriz, Souad; El Guemmat, Kamal; Mansouri, Khalifa
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.v14i2.8182

Abstract

This study examines the impact of information technology governance (ITG) on digital transformation (DT) in Moroccan higher education institutions, particularly emphasising the mediating role of absorptive capacity. Utilising a rigorous methodological framework, the research analyzes data collected from 110 staff members using structural equation modelling with the SmartPLS tool. The goal is to explore the complex dynamics between ITG practices and DT capability. The findings reveal a positive and statistically significant relationship between ITG mechanisms and absorptive capacity (AC) and between the latter and the success of DT. The study also identifies AC as a crucial mediator between ITG and digital capability (DC). It suggests universities should strengthen their AC and adopt open policies to increase their innovative potential. This contribution enriches the existing literature by empirically confirming the influence of certain IT governance variables on DC within Moroccan universities, offering valuable insights for academic researchers and practitioners involved in IT governance strategies and DT.
Predicting demand in changing environments: a review on the use of reinforcement learning in forecasting models Rolando Neira Villar, José; Angel Cano Lengua, Miguel
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.v14i2.8848

Abstract

This systematic review, carried out under the PRISMA methodology, aims to identify how reinforcement learning has been used in demand forecasting, distinguishing the problems they are trying to overcome, recognizing the algorithms used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying the business sectors in which it has been developed. Studies from all sectors were considered to expand the search range. A total of 24 articles were qualitatively analyzed, and the main results were that reinforcement learning has been used mainly for the selection or dynamic integration of the best predictors from a base of them to adapt to changing environments; whereas forecasting in volatile and complex environments is the main issue addressed; whereas Q-learning (QL), deep q network (DQN), double deep q network (DDQN), and deep deterministic policy gradient (DDPG) are the most widely used algorithms; and that, finally, the sectors of electric power, thermal energy, transport and telecommunications are the sectors where this type of forecast has been developed. Finally, given that all the models studied lack mechanisms for detecting concept drift, a new use of reinforcement learning for this purpose is proposed.
Application of feature-based image matching method as an object recognition method Karma, I Gede Made; Darma, I Ketut
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.v14i2.8803

Abstract

In everyday life, objects are recognized based on the suitability of their characteristics to familiar objects. A feature matching process occurs when recognizing objects. This concept is what we want to apply and test in this research. Because various factors can influence the level of accuracy and success of an image matching method, the first step taken is to improve the accuracy level of the image matching method used. There are three feature-based image matching methods, which are implemented as object recognition methods. These three methods are the result of modifications of the image matching function method, normalized 2D cross correlation method and point feature matching which were later named PICMatch, NCMatch and FBMatch. As image matching methods, these three modified methods show performance with a success rate above 95%. However, when applied as an object recognition method, both individually and combined, the three methods only have a maximum accuracy of 7%. These results are obtained by matching the samples using one of the methods with the best match rate, in the order of application of the PICMatch, NCMatch, and FBMatch methods.
An efficient course recommendation system for higher education students using machine learning techniques M. Arcinas, Myla; Meenakshi, Meenakshi; S. Bahalkar, Pranjali; Bhaturkar, Deepali; Lalar, Sachin; Pitambar Rane, Kantilal; Garg, Shaifali; Omarov, Batyrkhan; Raghuvanshi, Abhishek
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.v14i2.7711

Abstract

Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Beyond a simple filter: transient and steady state analysis of first-order resistor-resistor-capacitor circuits Djelaila, Soumia; Abderrazak Tadjeddine, Ali; Ilyas Bendjillali, Ridha; Sofiane Bendelhoum, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10166

Abstract

This paper presents a quantitative analysis of a first-order resistor-resistor-capacitor (RRC) circuit, detailing its transient, steady state, and frequency-domain behaviors through computational modeling. The study confirms that the circuit's time constant (τ) governs its dynamic response, with the capacitor charging to 63.2% of its final voltage in one τ. The key finding is the circuit's fundamental distinction from a simple resistor-capacitor (RC) filter: under a 100 V step excitation, the RRC topology stabilizes with a non-zero steady-state current of 0.35 A, following a controlled transient inrush of 1.0 A. Frequency analysis further characterizes the circuit as a stable low-pass filter with a predictable -20 dB/decade roll-off. This work elucidates a critical engineering trade-off, demonstrating that the RRC's components dually define its transient speed and its final steady state operating point, providing a quantitative framework for advanced power management and signal conditioning applications.
Application of traction force observer and sliding mode controller for speed in enhancing the stability of electric vehicles Thi Hoai Thu Anh, An; Van Hoa, Nguyen
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9653

Abstract

With the rapid advancement of electric vehicle (EV) technology, optimizing control and stability has become a key research focus. One major challenge is efficiently distributing traction force while minimizing disturbances under real-world conditions. This paper proposes a traction force observation method combined with a sliding mode speed controller to enhance EV performance. The observation method estimates the traction force from the motor to the wheels and detects disturbances affecting force transmission. This enables optimal traction force distribution among the wheels, reducing slip, improving road grip, and enhancing stability in complex driving conditions. Meanwhile, the sliding mode controller flexibly adjusts traction force as the vehicle navigates various terrains, ensuring stability and safety in hazardous situations. Simulations conducted using MATLAB Simulink and CarSim demonstrate that the proposed system significantly improves EV stability and control performance. Although these results are promising, further studies are necessary to address real-world implementation challenges and optimize the method for practical applications, including parameter tuning, sensor integration, and experimental validation. Overall, this research provides a practical framework for enhancing traction control and vehicle dynamics in future intelligent electric mobility systems.
Improvement of load frequency control performance for shipboard microgrid system Nguyen, Cong-Trang; Nghia Tin, Nguyen; Pham Thien Hao, Thai; Tan Liem, Phan
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9920

Abstract

This research studies the shipboard microgrid (MG) scheme's frequency fluctuations problem contrary to the impulsiveness of renewable resources, load instabilities, and the uncertainty of the parameters in the ship MG plant. A shipboard MG system consists of some of the renewable energy resource s (RESs) such as photovoltaic (PV), wind turbine generator (WTG), battery energy storage system (BESS), ship diesel generator (DG), fuel cell (FC), aqua electrolyzer (AE), and loads. A new fuzzy proportional integral derivative (FPID) controller is established to attain the desired frequency stability for the shipboard MG system. Additionally, various scenarios are executed in this research to validate the robustness of the anticipated controller to various load disturbances, parameter changes of plant, and fluctuations of solar irradiance and wind speed. The numerical simulation results obtained in three scenarios compared with those of the conventional PID controller and the existing time-varying derivative fractional order PID (TVD-FOPID) controller in literatures to validate the high usefulness and applicability of the planned control strategy. In brief, the established load frequency controller (LFC) based on FPID technique can improve frequency deviation in shipboard MG plant effectively.
A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems Venkata Rajanna, Bodapati; Rama Krishnaiah, Kondragunta; Ramaiah, Veerlapati; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Rao Kolukula, Nitalaksheswara; Sudhakar, Ambarapu
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10752

Abstract

Transformerless inverters are increasingly favored in grid-connected photovoltaic (PV) systems due to their higher efficiency, reduced size, and lower cost. This paper presents a novel transformerless inverter topology that integrates buck boost conversion with an advanced fuzzy logic controller (FLC) to enhance energy extraction and power quality under dynamically changing solar conditions. The proposed system employs a sine triangle pulse width modulation (PWM) scheme in conjunction with the FLC to improve waveform quality and system responsiveness. By dynamically adapting to variations in irradiance and load, the control strategy reduces the total harmonic distortion (THD) from 36.51% to 1.51%, significantly enhancing compliance with international grid standards. Additionally, a novel grounding technique is implemented to mitigate common mode leakage currents, a typical issue in transformerless systems, without the need for galvanic isolation. Comprehensive MATLAB/Simulink simulations validate the inverter’s performance, demonstrating superior dynamic behavior, harmonic suppression, and overall reliability. The proposed architecture offers a compact, cost effective, and high performance solution for next generation grid integrated solar PV systems.

Page 283 of 291 | Total Record : 2901

 281   282   283   284   285 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 6: December 2024 Vol 13, No 5: October 2024 Vol 13, No 4: August 2024 Vol 13, No 3: June 2024 Vol 13, No 2: April 2024 Vol 13, No 1: February 2024 Vol 12, No 6: December 2023 Vol 12, No 5: October 2023 Vol 12, No 4: August 2023 Vol 12, No 3: June 2023 Vol 12, No 2: April 2023 Vol 12, No 1: February 2023 Vol 11, No 6: December 2022 Vol 11, No 5: October 2022 Vol 11, No 4: August 2022 Vol 11, No 3: June 2022 Vol 11, No 2: April 2022 Vol 11, No 1: February 2022 Vol 10, No 6: December 2021 Vol 10, No 5: October 2021 Vol 10, No 4: August 2021 Vol 10, No 3: June 2021 Vol 10, No 2: April 2021 Vol 10, No 1: February 2021 Vol 9, No 6: December 2020 Vol 9, No 5: October 2020 Vol 9, No 4: August 2020 Vol 9, No 3: June 2020 Vol 9, No 2: April 2020 Vol 9, No 1: February 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 List of Accepted Papers (with minor revisions) More Issue