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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Evaluating multilingual encoder models for few-shot named entity recognition tasks Bouabdallaoui, Ibrahim; Guerouate, Fatima; Bouhaddour, Samya; Saadi, Chaimae; Sbihi, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp745-757

Abstract

This work provides a thorough analysis of few-shot learning approaches in the realm of multilingual named entity recognition (NER). Our research is driven by the need to enhance linguistic inclusivity and performance efficiency across diverse languages. We focus on benchmarking a selection of prominent encoder models including XLM-RoBERTa (XLM-R), multilingual BERT (mBERT), DistilBERT, character architecture for eNcoders IN embeddings (CANINE), and multilingual text-to-text transfer transformer (mT5), to illuminate their capabilities and limitations within few-shot learning paradigms, particularly for underrepresented languages. Results indicate that models like XLM-R and mT5 demonstrate superior adaptability and accuracy, outperforming others in complex linguistic settings, which suggests their potential in supporting more inclusive artificial intelligence (AI) technologies. The impact of this study extends beyond academic interest, offering pivotal insights for the development of more inclusive, adaptable and efficient NER systems. By advancing our understanding of few-shot learning in multilingual contexts, this work contributes to the broader goal of creating AI applications that are linguistically diverse and more reflective of global communication patterns. These results provide crucial insights for advancing entity recognition capabilities across diverse artificial intelligence systems, facilitating development of more precise, equitable, and sophisticated linguistic processing frameworks.
A deep learning-integrated proxy model for efficient cryptocurrency payments Kasula, Vinay Kumar; Yadulla, Akhila Reddy; Konda, Bhargavi; Yenugula, Mounica; Ayyamgari, Supraja
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1023-1039

Abstract

Blockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy.
Detection of short circuit faults in two-level voltage source inverter using convolution neural network Aioub, Sai; Zakariya, Belghiti; Lamiaâ, El Menzhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp580-589

Abstract

Voltage source inverters (VSIs) play a critical role in modern industrial systems, particularly in controlling the operation of equipment such as induction motors. Ensuring their reliable performance is crucial, as faults like short circuits can severely disrupt industrial processes. This paper introduces a new diagnostic approach for detecting and localizing short circuit faults in VSIs. The method uses Lissajous curves derived from the Clark transformation of the VSI’s 3-phase voltage components (Vα, Vβ). These curves serve as input data for a convolutional neural networks (CNNs) model, enabling the accurate classification of single and double short circuit faults. Simulation results using MATLAB/Simulink demonstrate that the proposed method achieves 100% classification accuracy within 100 ms, highlighting its suitability for real-time applications. The approach offers significant advantages in speed and accuracy over traditional techniques, with potential implications for enhancing the reliability and safety of inverter-driven systems in industrial environments.
Robot vision and virtual reality integration to help paralyzed patients mobility Jalil, Abdul; Suparno, I Wayan
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp610-618

Abstract

This study aims to develop a device that can assist the mobility of paralyzed patients, enabling them to communicate with family and caregivers by integrating robot vision and virtual reality (VR). The method used to connect audio and visual data communication between robot vision and VR is by utilizing the robot operating system (ROS2) middleware communication node through topics over a wireless network. In this research, paralyzed individuals can maneuver based on the movement direction of robot vision, which is remotely controlled via a joystick through Bluetooth communication. The input devices used in this system include a camera, microphone, joystick, and ultrasonic sensors. The processing part uses a Raspberry Pi as the data processing center, and the output includes a DC motor, servo motor, speaker, 5-inch monitor, and headset. The results indicate that the integration of robot vision and VR can assist paralyzed individuals in communicating with family or caregivers at distances of up to 10 meters. This is due to the maximum joystick control range for moving the robot via Bluetooth communication being 10 meters. Furthermore, this study shows that the use of robot vision and VR can improve paralyzed patients’ motivation, supporting the medical field in patient care.
Efficient lung disease detection using a hybrid vision transformer and YOLO framework with transfer learning Khan, Kashaf; Aleem, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1141-1148

Abstract

Lung diseases are among the most important causes of morbidity and mortality worldwide; it require prompt and accurate diagnosis methods. A novel hybrid deep learning framework for integrating you only look once version 8 (YOLOv8), considering real-time detection and vision transformer (ViT-B/16) for global context-based classification of lung diseases in chest X-ray images, is presented. Based on transfer learning and a two-stage detection-classification pipeline, this proposed model is applicable to dealing with inter-image variability, overlapped disease features and lack of annotated medical examples. Our developed hybrid model achieves the highest classification accuracy of 96.8% and 0.98 AUC-ROC on the National Institutes of Health (NIH) Chest X-ray dataset, which consists of over 112,000 images covering 14 diseases, and outperforms its several current state-of-the-art models. In addition, attention heatmaps and bounding box visualizations highly correlate with clinical variables and enhance interpretability. This paper demonstrates the practicability of hybrid vision driven architectures for better medical image analysis and shows their integration into clinical decision-support systems.
Experimental analysis and bug abstraction for distributed computation on ray framework Sinaga, Arnaldo Marulitua; Nainggolan, Wordyka Yehezkiel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp789-800

Abstract

This research aims to address challenges in distributed computing, focusing on the ray framework, which has potential for efficient parallel and distributed task execution. While methods such as model-checkers and fuzzing have been applied to detect bugs, both have limitations in handling the complexity of distributed computing, particularly in dealing with issues like state-space explosion and identifying rare bugs. This study proposes an alternative approach through experimental analysis and bug abstraction methods to discover, identify, and classify bugs in the ray framework. Experimental analysis involves isolating and re-testing bugs in a controlled environment to understand their characteristics, while bug abstraction analyzes the factors causing bugs to identify common patterns and characteristics. The results of this research successfully identified three main categories of bugs: crash, performance, and inaccurate status, and revealed bug characteristics that do not depend on actor instance multiplicity, actor type, specific event sequences, or particular configurations. This research makes a significant contribution to the development of more effective and efficient bug detection methods in distributed computing, particularly in the ray framework, and paves the way for further research to enhance the reliability of distributed systems. 
Evaluation of the impact of machine learning on the prediction of residential energy consumption Machaca-Casani, Richar Martín; Figueroa-Mayta, Luis Alfredo; Contreras-Nuñez, Joel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp567-579

Abstract

The objective of this research was to compare the performance of machine learning models and traditional statistical methods for the prediction of residential energy consumption, using a dataset with relevant variables such as consumption, temperature, time of day, type of housing, and energy usage habits. A quantitative and comparative methodology was applied, involving data preprocessing, variable encoding, and normalization, as well as division into training and testing sets. The random forest, support vector machine (SVM), deep neural network (MLP), and linear regression models were trained and evaluated using standard metrics such as mean absolute error (MAE), root mean squared error (RMSE), and R² on test and cross-validation sets. Results show that SVM and linear regression achieved better accuracy and generalization capability, while random forest and the deep neural network exhibited lower explanatory power, reflected in negative R² values. Using the trained models, a projection of residential energy consumption for the 2026–2030 period was performed, revealing a generally increasing trend across all models, although with differences in the magnitude of the predictions. In conclusion, under the current conditions, traditional models demonstrate greater robustness, highlighting the need to tailor algorithm selection to the data context. These projections provide a valuable tool for future energy planning.
Laryngeal pathology detection using EMD-based voice acoustic features analysis and SVM-RBF Cherif, Sofiane; Kaddour, Abdelhafid; Benkada, Abdelmoudjib; Karoui, Said; Bahi, Ouissem Chibani; Daho, Asmaa Bouzid
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp640-653

Abstract

Traditional techniques for detecting laryngeal pathologies, such as laryngoscopy and endoscopy, are costly and invasive. This study presents a novel approach for detecting laryngeal disorders using empirical mode decomposition (EMD)-based acoustic features analysis and support vector machine (SVM) with a radial basis function (RBF) kernel. The experiments were conducted using the Saarbrucken voice database (SVD). The voice signals were then decomposed using EMD to extract the intrinsic mode functions (IMFs). The IMF with the highest energy value was selected as the most relevant. A set of acoustic features, including mel-frequency cepstral coefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), Pitch (fundamental frequency), higher-order statistics (HOSs), zero-crossing rate (ZCR), spectral centroid (SC), and spectral roll-off (SRO), is derived from the most relevant IMFs and fed into an SVM classifier to differentiate between healthy and pathological voices. Experimental results demonstrate the effectiveness of the proposed methodology, achieving a high classification accuracy of 94.5%, a sensitivity of 94.2%, a specificity of 95.3%, and an F1 score of 96.1%, outperforming conventional approaches. These results highlight the potential of EMD-based voice analysis as a non-invasive and reliable tool for early diagnosis of laryngeal disorders.
A novel approach for detection of cracks in painting and concrete surface images using CNN models Vadicherla, Deepti; Gupta, Poonam
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp988-1000

Abstract

Discovering the beginnings of historical artworks takes one on an amazing voyage across space and time. People all around the world have been captivated by India's rich cultural heritage throughout its history, and ancient paintings have always been a very important part of it. Over the period of time, these ancient paintings can get cracks on it due to many factors. This research introduces an automated image classification system where the cracks on the paintings as well as the concrete surface will get detected. Detecting cracks on the concrete surface is important because the longevity and upkeep of concrete structures rely on the prompt identification and treatment of cracks, which can weaken the structure and necessitate expensive repairs. In this study, we focus on image classification using general convolution neural network (CNN), Inception V3, VGG-16, and ResNet-50 models of CNN. These models are trained and validated separately on two different datasets of paintings and concrete surfaces. Inception V3 and VGG-16 models achieve high accuracy, respectively in painting and concrete datasets in comparison with general CNN and ResNet-50 models.
Effect of binaural beat brainwave entrainment on brainwave ratios in students with learning difficulties Kanhere Banait, Shweta; Ranjan, Prabhat; More, Rajendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp916-925

Abstract

This study examined the impact of binaural beat brainwave entrainment (BB BWE) on cognitive function and learning performance (LP) in children aged 8-13 with learning difficulties. A group of 52 participants was divided into a test group (TG) receiving BB BWE for four weeks and a control group (CG) without intervention. Results showed significant improvements in the TG, with LP increasing by up to 78% by week 4 according to cognitive assessment methods. EEG data corroborated these findings, showing a 74% improvement in TG students’ performance. Favorable changes in Electroencephalography (EEG) ratios were observed, including decreased theta/beta and theta/alpha ratios and an increased alpha/beta ratio. Topographical EEG maps revealed more balanced brain activity patterns post-BWE. The CG showed no significant changes. Notably, performance in the TG declined after discontinuing BWE, suggesting the need for ongoing intervention to maintain benefits. These findings indicate that BB BWE could be an effective non-invasive method for enhancing cognitive function and learning capacity in individuals with learning difficulties. However, further research is needed to establish long-term effects and optimal application protocols.

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