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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 56 Documents
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Search results for , issue "Vol 40, No 2: November 2025" : 56 Documents clear
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.
A systematic evaluation of pre-trained encoder architectures for multimodal brain tumor segmentation using U-Net-based architectures Abbas, Marwa; Khalaf, Ashraf A. M.; Mogahed, Hussein; Hussein, Aziza I.; Gaber, Lamya; Mabrook, M. Mourad
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.pp850-859

Abstract

Accurate brain tumor segmentation from medical imaging is critical for early diagnosis and effective treatment planning. Deep learning methods, particularly U-Net-based architectures, have demonstrated strong performance in this domain. However, prior studies have primarily focused on limited encoder backbones, overlooking the potential advantages of alternative pretrained models. This study presents a systematic evaluation of twelve pretrained convolutional neural networks—ResNet34, ResNet50, ResNet101, VGG16, VGG19, DenseNet121, InceptionResNetV2, InceptionV3, MobileNetV2, EfficientNetB1, SE-ResNet34, and SE-ResNet18—used as encoder backbones in the U-Net framework for identification and extraction of tumor-affected brain areas using the BraTS 2019 multimodal MRI dataset. Model performance was assessed through cross-validation, incorporating fault detection to enhance reliability. The MobileNetV2-based U-Net configuration outperformed all other architectures, achieving 99% cross-validation accuracy and 99.3% test accuracy. Additionally, it achieved a Jaccard coefficient of 83.45%, and Dice coefficients of 90.3% (Whole Tumor), 86.07% (Tumor Core), and 81.93% (Enhancing Tumor), with a low-test loss of 0.0282. These results demonstrate that MobileNetV2 is a highly effective encoder backbone for U-Net in extracting tasks for tumor-affected brain regions using multimodal medical imaging data.
Vehicle recognition on indian roads using data augmentation and VGG-16 model K. L., Arunkumar; K. M., Poornima; Danti, Ajit; H. T., Manjunatha
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.pp1177-1186

Abstract

In an advanced intelligent transportation system vehicle recognition and classi f ication is very significant. In current research trend, recognition of vehicles is done byusingmachinelearning (ML)andcomputervisiontechniques. Vehicle’s multi-view images or videos with different lighting conditions are annotated and given to the deep neural network to build an automated system to recognize the vehicles models. The augmentation of data can increase the number of sam ples in learning, with the small available datasets. Geometric transformations, brightness changes, and different filter operations are applied to the data through data augmentation. Furthermore, be orthogonal experiments we determine the optimal data augmentation method to obtain 96% accuracy in results. Detailed information is reported based on the classification of four different types of vehi cles and the results show that convolutional neural network with 16 layers deep techniques are effective in solving challenging tasks while recognizing moving vehicles.
Ensemble recursive feature elimination-based ensemble classification for medical diagnosis Ramanathan, Thirumalaimuthu Thirumalaiappan; Hossen, Md. Jakir; Al Mamun, Abdullah; Raja, Joseph Emerson
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.pp758-771

Abstract

The application of data mining techniques for the extraction of patterns from medical datasets is useful in the prediction of various diseases from the data of patients. An appropriate feature selection method is required for the medical datasets to give better results for the medical data mining process. In data preprocessing, feature selection is an important process that finds the most relevant features from the dataset. Considering all features of the medical dataset without using any feature selection process may sometimes lead to inaccurate results. Most of the medical datasets contain meaningless data that are not relevant to the data mining process. These data can be eliminated through the feature selection process. This paper presents an integration of an ensemble feature selection approach and an ensemble classification approach through a classifier called the ensemble recursive feature elimination-based ensemble classifier (ERFE-EC) for the classification of medical data. Four different medical datasets were used for testing the ERFE-EC method, which showed promising results.

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