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 75 Documents
 1   2   3   4   5 
Search results for , issue "Vol 14, No 3: June 2025" : 75 Documents clear
Factorized cross entropy integrated hyperspectral CNN (HSCNet-FACE) for hyperspectral image classification C. Patil, Pawankumar; Sonnad, Shashidhar
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8829

Abstract

The use of hyperspectral image classification algorithms has garnered increasing interest from the scientific community in recent years, especially in the field of geosciences for pattern recognition applications. In order to extract full spectral-spatial characteristics, this study presents feature extraction with hyperspectral CNN (HSCNet), a unique hierarchical neural network architecture. HSCNet can handle computational complexity issues and capture extensive spectral-spatial information with ease. We use factorized cross entropy (FACE) to address the common problem of class imbalance in both experimental and real-world hyperspectral datasets in order to construct an accurate land cover classification system. FACE makes it easier to reconstruct the loss function, which helps to effectively accomplish the goals that have been expressed. We provide a new framework for hyperspectral image (HSI) classification called FACE, which combines components from HSCNet and FACE. Next, we carry out in-depth studies using two different remote sensing datasets: Botswana (BS) and Indian Pines (IP). We compare the effectiveness of different backbone networks in terms of categorization and compare its classification performance under various loss functions. Comparing our suggested classification system against the state-of-the-art end-to-end deep-learningbased techniques, we find encouraging results
Benchmarking machine learning algorithm for stunting risk prediction in Indonesia Novalina, Nadya; Aksar Tarigan, Ibrahim Amyas; Kayla Kameela, Fatimah; Rizkinia, Mia
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8997

Abstract

Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75%. Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges as the best classification, with oversampling yielding superior performance.
An economical approach of structural strength monitoring utilizing internet of things S. Nayak, Deekshitha; Kumar Pandey, Anubhav
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8752

Abstract

In the current environment, structural health monitoring (SHM), has become increasingly important. The cost of sensors and connectivity has significantly decreased, allowing for remote data gathering for critical analysis and structure monitoring. This allows for the assessment and improvement of the structures' residual lifespan. The internet of things (IoT) is a network of intelligent sensors that combines the identification and detection followed by sending the different structural responses to remote computers for further analysis i.e., processing and monitoring. In this work, an integrated IoT platform for damage detection is proposed which includes an Arduino, Wi-Fi module, and sensors. The sensors gather responses from the host structure which follows a precise mathematical model is introduced to determine and measure the structural damage in comparison to the reactions of the structural member that is in good health. To determine the degree of damage, the responses recorded from the damaged and healthy beams are analyzed using the cross-correlation (CC) damage index. Moreover, the analysis carried out reveals the CC values are uploaded to the cloud, where, if the CC value is over the threshold limit, a mobile warning message is delivered.
Optimizing fatigue life predictions for scraper rings: classical vs modern models Fatihi, Sophia; El Hasnaoui, Yassine; Ouabida, Elhoussaine; Mharzi, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9256

Abstract

This study provides a comprehensive comparative evaluation of classical and modern predictive models for fatigue life in scraper rings of internal combustion engines, which operate under high thermo-mechanical stresses. Accurate fatigue life predictions are essential for optimizing engine component design, preventing both over- and under-engineering while ensuring long-term reliability. The effectiveness of both traditional models and newer advanced approaches was analyzed using loading profiles that replicate real-world engine operating conditions. Results indicate that stress-life models offer more reliable predictions for high-cycle fatigue scenarios, while strain-life models perform better under low-cycle fatigue conditions. Furthermore, fracture mechanics models show great promise in predicting crack propagation and identifying failure mechanisms. Detailed inspections and Légraud-Poirier (LP) tests confirmed fatigue-induced cracking at critical locations of the scraper rings, emphasizing the importance of incorporating multi-axial loading in fatigue assessments. The findings underscore the necessity for using comprehensive loading profiles and thorough inspections to enhance the accuracy and dependability of fatigue life predictions, which are critical for improving the performance and durability of engine components.
Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics Kumar Singh, Siddhanta; Sharma, Anand
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9398

Abstract

This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swerving, and sideslipping). The RF classifier achieved the highest accuracy, which reinforces the strength of the chosen models. The inclusion of comparisons with other techniques supports arguments about the model's performance. The related works section connects the references to the central topic by highlighting prior approaches and research studies related to OBD II and driver behaviour analysis. The goals of this study are improving the accuracy of driving behaviour classification, with implications for traffic safety, driver education, and insurance sectors.
New model for emotion detecting from French text using bidirectional long short-term memory Adel, Aya; A. Taie, Shereen; Elhariri, Esraa; Hasan Ibrahim, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8755

Abstract

Due to the fast growth of social networks, humans have transformed from being general users to creators of network information’s by providing reviews, evaluations, and thoughts on social websites expressing their feelings on various topics. Recently, feedback analysis has become important not only for business owners to improve their products based on user feedback, but also for users to help them select the most suitable products by benefiting from other's experiences. Extracting and identifying human emotional states such as happiness, anger, and worry in texts are targets of emotion analysis due to their importance in providing suggestions for companies and users according to their needs. Although, there has been a lot of work on emotion detection in English text, there is currently lack of research on French text that is because of not existing of French emotion dataset. This paper presents an emotion detection model that integrates the Camembert tokenizer with bidirectional long short-term memory (Bi-LSTM) for emotion detection in French text. The proposed model is trained and validated using a dataset that has been annotated for emotions in French. The proposed model achieved accuracy and an F1-score of 98.66% and 98.66%, respectively, outperforming previous work by 26.36%.
Drone-based high-resolution air pollution monitoring: a comprehensive system and field evaluation Neole, Bhumika; Vyawahare, Shreerang; Pinjarkar, Latika; Kohli, Tanishq; Sah, Parimal; Panchore, Meena
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8592

Abstract

This paper presents a novel air pollution monitoring system designed for drone deployment, featuring a specialized payload comprising sensor suites and processing components. The upper half of the payload accommodates MQ series sensors and an SDS011 particulate matter (PM) sensor, strategically positioned to provide comprehensive coverage of various air pollutants. Processing boards, including an Arduino and ESP8266 node micro controller unit (NodeMCU), facilitate data collection, transmission, and connectivity to a designated cloud platform for real-time monitoring and analysis. Additionally, the payload incorporates air pumps for pollution mitigation and relay modules for remote control. Field tests conducted in suburban and industrial areas evaluated the system's efficacy in capturing subtle air quality variations and responding to pollution spikes. Analysis of ground-level and airborne data provided insights into sensor performance and system adaptability across diverse environments. Overall, the proposed system demonstrates promising potential as a comprehensive solution for high-resolution air pollution monitoring, with implications for enhancing public health interventions and environmental management strategies.
Deep learning-based cellular traffic prediction for 4G long-term evolution networks using three models Shindou, Hassnaa; El Hasnaoui, Yassine; Mohamed Nabil, Srifi
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9221

Abstract

Wireless networks can be seen as the essential element of contemporary communication systems, connecting, in one way or another, billions of people and technologies all over the world. As a result, there is more of requirement from the area of application for models, which should be able to help in the analysis of the time series of mobile traffic to enhance the quality of service (QoS) in the present networks as well as in the future ones. The primary objective of this article is to develop effective artificial intelligence (AI) models for traffic load prediction in cellular networks. To achieve this, we employ three models; gated recurrent unit (GRU), bidirectional long short time memory (BiLSTM), and long short time memory (LSTM), to make numerical estimates of the network traffic at 4G long-term evolution (LTE) cell towers. The empirical results indicate that the BiLSTM model outperforms both the LSTM and GRU models, achieving root mean squared error (RMSE), mean absolute error (MAE), and R2 values of 86.64, 67.12, and 93.23%, respectively. Although this research focuses on traffic modeling for 4G LTE networks, the proposed models hold significant value for the development and optimization of the upcoming generations.
BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting Maria, Evi; Wahyono, Teguh; Dwi Hartomo, Kristoko; Purwanto, Purwanto; Arthur, Christian
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8653

Abstract

This paper presents bidirectional long short-term memory (BiLSTM) OptiFlow, an optimized deep learning model designed to predict the financial health of cooperatives using key financial ratios: debt to equity ratio (DER), net profit margin (NPM), and return on equity (ROE). By leveraging a BiLSTM architecture combined with an optimal decayed learning rate, this model aims to enhance forecasting accuracy. The proposed model was tested against three established methods—recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—and evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) metrics. Results indicate that BiLSTM OptiFlow outperforms the other models across all key indicators. This research offers a robust approach to cooperative financial forecasting, with significant implications for decision-making processes in cooperative management.
A novel switched-capacitor multilevel inverter for efficient voltage level generation Ezhilvannan, Parimalasundar; Venkata Padmavathi, Annam; Hani, Ummi; Panchkumar Dhote, Vyanktesh; Hemanth Kumar, Busireddy; Ramnarayan Singh, Arvind
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9208

Abstract

This paper presents a novel single direct current (DC) source with switched-capacitor multilevel inverter (MLI) architecture capable of achieving seven output voltage levels using only eight switches, one diode, and two capacitors. The proposed topology (P) is compared with recent MLI configurations to assess its efficiency and performance. MATLAB/Simulink tools are utilized for simulation studies, and experimental validation is conducted to corroborate the theoretical findings. The investigation explores the impact of modulation index and switching frequency variations on the P output characteristics. Results indicate that the proposed MLI topology offers significant advantages in terms of component count reduction and simplicity while maintaining competitive performance compared to state-of-the-art alternatives. Additionally, the study provides insights into the influence of modulation index and switching frequency changes on the output voltage waveform, highlighting the adaptability and robustness of the P under varying operating conditions. This research contributes to the advancement of MLI designs by offering a streamlined and efficient solution suitable for various power electronic applications, including renewable energy systems and motor drives, where minimizing component count and complexity are crucial design considerations.

Page 1 of 8 | Total Record : 75

 1   2   3   4   5 

Filter by Year

2025 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