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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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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.
Detecting spam using Harris Hawks optimizer as a feature selection algorithm Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Nabil Alkhatib, Sumaya; Shambour, Qusai Y.; Alsaaidah, Adeeb M.
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.v14i3.9198

Abstract

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.
Improved imperceptible engagement-based 2D sigmoid logistic maps, Hill cipher, and Kronecker XOR product Lestiawan, Heru; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Purwanto, Purwanto; Sari, Christy Atika; Doheir, 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.v14i3.8331

Abstract

Image encryption is a crucial facet of secure data transmission and storage, and this study explores the efficacy of combining sigmoid logistic maps (SLM), Hill cipher, and Kronecker's product method in enhancing image encryption processes. The evaluation, conducted on diverse images such as Lena, Rice, Peppers, Cameraman, and Baboon, unveils noteworthy findings. The Lena image emerges as the most successfully encrypted, as evidenced by the lowest mean squared error (MSE) at 92.81 and the highest peak signal-to-noise ratio (PSNR) at 19.43, reflecting superior fidelity and quality preservation. Additionally, the encryption of 64×64 pixels images consistently demonstrate robustness, with a high number of pixels change rate (NPCR) and unified average change intensity (UACI) values, particularly notable for the Cameraman image. Even for 128×128 pixels images, commendable encryption performance persists across the tested images. The amalgamation of SLM, Hill cipher, and Kronecker's product emerges as an effective strategy for balancing security and perceptual quality in image encryption, with the Lena image consistently outperforming others based on comprehensive metrics. This research provides valuable insights for future studies in the dynamic domain of image encryption, emphasizing the potential of advanced cryptographic techniques in ensuring secure multimedia communication.
Development of a robust and sustainable regional demography-based demand management technique Ganguly, Ayandeep; Kumar Sil, Arindam
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.v14i3.9133

Abstract

This paper presents a robust and sustainable energy management system driven by regional demographic patterns developed using fuzzy logic and mixed integer linear programming (MILP). This method detects and integrates variations in the energy use patterns of urban and rural communities attaining improved efficiency in the management of regional power demand. The detection and integration of the urban and rural energy use patterns were done by combining period partitioning based regional time of use tariff and fuzzy based appliance level renewable resource allocation to develop a function to be optimized using an improved MILP which provides users with the optimum schedule of appliance usage based on their demographic classification. The effectiveness of the proposed method was tested by running MATLAB simulations of different scenarios emulating continuous regional renewable integration planning with urban and rural power consumption profiles generated using LoadProGen. The proposed method’s effectiveness is confirmed by the achievement of a reduction upto 31% in the community energy cost as well as significant reduction in the energy costs of each participant over different scenarios compared to the unoptimized base case. The proposed method can be effectively utilized in energy management applications catering to multiregional and mixed demographic communities.
Automatic urinary bladder detection from medical computed tomography scans using convolutional neural network Mahdy Omran, Lamia Nabil; Ali Ezzat, Kadry; El Fadaly, Hossam Ahmed; I. Hussein, Aziza; Gameil Shehata, Emad; Mansour Salama, Gerges
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.v14i3.8975

Abstract

This paper introduces a system for detecting and evaluating an algorithm that segments the urinary bladder in medical images obtained from contrast-less computed tomography (CT) scans of patients with bladder tumors. Multiple segmentation methods are needed in situations where tumors in the bladder cause structural changes that appear as irregularities in images, complicating the slicing process. The segmentation process begins with viewing the urinary bladder DICOM in three different perspectives, and then enhancing the image to expand the dataset. Next, the areas of the urinary bladder are pinpointed, with the urinary bladder dataset being split into 70% for training and 30% for testing to distinguish it from the nearby tissues, organs, and bones. The suggested system was evaluated on eight 3D CT images obtained from the cancer imaging archive (TCIA). Results from the experiment show that the designed system is effective in identifying and delineating the urinary bladder.

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