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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 287 Documents
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Energy Efficient Battery Optimization Model (EE-BOM) using Machine Learning Algorithms and Harris Hawks Optimization Shanmugam, Sathishkumar; Rajkumar, R. Yogesh
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.635

Abstract

Electric vehicles (EVs) are gaining popularity because of their cheap running costs and positive environmental impacts. However, EVs' limited battery life is one of their biggest drawbacks. The Energy Efficient Battery Optimisation Model (EE-BOM), a unique model for early battery life detection, is presented in this work. This study makes use of a dataset from the Hawaii Natural Energy Institute that includes 14 distinct batteries that were put through more than 1000 cycles in a controlled environment. A multi-step approach is used, with feature selection coming after data collection and preprocessing with data normalisation. Additionally, for early RUL prediction, the XGBoost Approach, which combines Harris Hawk Optimisation (HHO) with Artificial Neural Networks (ANN), is used. Finding important factors affecting battery health and longevity is made easier with the help of feature importance analysis. Outlier reduction improves model accuracy, and statistical analyses show no missing or redundant data. Notably, with almost flawless predictions, XGBoost proved to be the most successful algorithm. This study emphasises how important RUL prediction is for improving battery lifetime management, especially in applications like electric cars, guaranteeing the best possible use of resources, economic viability, and environmental sustainability.
Improving Classification of Medical Images Using ESRGAN-Based Upscaling and MobileNetV2 Masluha, Ida; Azhar, Yufis
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.636

Abstract

Low-resolution photos are frequently problematic in the medical field when diagnosing skin and eye conditions since they can induce noise and lower the precision of classification algorithms. To overcome this, this research implements the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method which is used to perform upscaling, namely increasing the resolution of a low image to a high-resolution image. The research results show that ESRGAN is able to improve the quality of eye and skin images, as proven by accuracy consistency tests on the two datasets. For image classification, the MobileNetV2 model is used because this model is suitable for eye and skin datasets. Evaluation of the image retrieval system using a high-resolution dataset resulting from ESRGAN Upscaling shows an increase in accuracy of 4-17% on both datasets. In this research, the improvement in visual image quality is also proven by the high Peak Signal-to-Noise Ratio (PSNR) value, so that ESRGAN is proven to be effective in increasing image resolution and clarity, both for eye medical image datasets and skin images.
Application of Hybrid Metaheuristic Algorithms for Feature Selection in Event-Related Potential Classification in Problematic Gamers Using Electroencephalograph Signal Wijayanto, Inung; Hadiyoso, Sugondo; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.638

Abstract

Online games have become a popular form of entertainment, particularly for relieving stress, and the rise in online gaming has led to an increase in problematic gaming behaviors. Excessive use of the internet for gaming has raised concerns about its neurophysiological impact, particularly on cognitive and emotional functions. Electroencephalogram signal and Event-Related Potential analysis are valuable tools for monitoring these effects. Given the vast amount of features that can be extracted from EEG signals, it is crucial to apply efficient feature selection methods to identify the most informative ones. This study utilizes the Go/No-Go Association Task combined with the recording of 16-channel EEG signals, chosen as the data-recording method to observe the response of individuals who are problematic online gamers to several stimulus themes. In this context, metaheuristic algorithms like Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization are employed to enhance feature selection. A hybrid approach, combining one of these methods with Binary Stochastic Fractal Search is proposed to improve classification accuracy and optimize feature selection. The results demonstrate that the hybridization of the best algorithm with B-SFS successfully selects the optimal features, achieving perfect classification performance, with an accuracy, sensitivity, and specificity of 1.00 for all respondents. This emphasizes the effectiveness of B-SFS, particularly its diffusion process, where Gaussian distribution facilitates the search for the best solution, thereby improving the reliability of feature selection for detecting problematic gaming behavior.
Optimized PCA Infused Liquid Neural Network Deployment for FPGA-Based Embedded Systems Deka, Bhupesh; Chatterjee, Sayanti; Chintalapudi, S Rao; Nikitha, Jajala; Kodavali, Lakshminarayana; Babu, Kancharagunta Kishan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.681

Abstract

The integration of neural networks into FPGA-based systems has revolutionized embedded computing by offering high performance, energy efficiency, and reconfigurability This paper introduces a novel optimization framework integrating Principal Component Analysis (PCA) to reduce the complexity of input data while preserving essential features for accurate neural network processing. By applying PCA for dimensionality reduction, the computational burden on the FPGA is minimized, enabling more efficient utilization of hardware resources. Combined with hardware-aware optimizations, such as quantization and parallel processing, the proposed approach achieves superior performance in terms of energy efficiency, latency, and resource utilization. Simulation results demonstrate that the PCA-enhanced Liquid Neural Network (LNN) deployment significantly outperforms traditional methods, making it ideal for edge intelligence and other resource-constrained environments. This work emphasizes the synergy of PCA and FPGA optimizations for scalable, high-performance embedded systems. A comparison study using simulation results between cascaded feed forward neural network (CFFNN), deep neural network (DNN) and liquid neural network (LNN) has been encountered here for the embedded system to show the efficacy of PCA based LNN. It has been shown from case studies that the average F1score is 98% in case of proposed methodology and accuracy is also 98.3% for high clock value.
Characterization of Si0.25Ge0.75 -FinFET as a Temperature Nano Sensor YOUSIF ATALLA; Mohamad Hafiz Mamat; Hasyim, Yasir
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.695

Abstract

This study addresses the impact of thermal sensitivity on the performance of FinFET transistors, with a focus on the role of different channel lengths in enhancing electrical performance. As semiconductor technology advances, understanding the effects of temperature on electronic devices becomes essential to ensure their stability in various applications. Semiconductor manufacturing has stuck to Moore's law, which requires reducing the size of transistors to magnify integration and reduce costs, but biosensors with classic planar transistors are receptive to the effects of short channels, leading to increased power wastage and minimizing sensitivity. FinFET structure shows higher-level gate control, increased repression of short-channel issues. The research uses simulation techniques to study the current-voltage (I-V) behavior in a FinFET structure that relies on Si0.25Ge0.75 as the channel semiconductor material. The effect of different temperatures (275, 300, 325, and 350 °K) with channel lengths of 10, 20, and 30 nm is analyzed, focusing on the change in current (∆I) within the operating voltage range of 0 to 1 V (VDD). The results reveal that thermal sensitivity increases with the reduction of channel length, especially between 10 and 20 nanometers, where the optimal length is found to be 20 nm, due to its balance between low threshold voltage and reduced drain-induced barrier lowering (DIBL), while maintaining stable performance within the studied temperature range. The study also showed that the device operates efficiently at a drain voltage (Vd) of 0.9 volts, ensuring stable performance in the range of 325-350 Kelvin. These results highlight the importance of adjusting design parameters to improve the thermal response of FinFETs, contributing to the development of semiconductors and their applications in nanotechnology and advanced electronics.
Analysis of the Impact of Data Oversampling on the Support Vector Machine Method for Stroke Disease Classification Luh Ayu Martini; Pradipta, Gede Angga; Huizen, Roy Rudolf
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.698

Abstract

Data imbalance is a critical challenge in the classification of medical data, particularly in stroke disease prediction, a life-threatening condition requiring immediate intervention. This imbalance arises due to the disproportionate number of non-stroke cases compared to stroke cases, which can lead to biased models favoring the majority class. Consequently, the model may struggle to correctly identify stroke cases, resulting in lower recall and an increased risk of misdiagnosis. This study evaluates the impact of various oversampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, SMOTE-Edited Nearest Neighbor (SMOTE-ENN), and SMOTE-Instance Prototypes Filtering (SMOTE-IPF), along with feature selection using Information Gain and Chi-Square, to assess their influence on model performance. Oversampling is utilized to address class imbalance by generating synthetic samples, thereby improving the representation of the minority class. Feature selection is employed to eliminate irrelevant or redundant features, enhancing both interpretability and computational efficiency. The dataset obtained from Kaggle, consists of 5,110 records and 12 features. Support Vector Machine (SVM) is used as the classification algorithm, with evaluations conducted on Linear, Radial Basis Function (RBF), and Polynomial kernels. Experimental results indicate that the highest performance is achieved by the combination of Borderline-SMOTE and the RBF kernel, yielding an accuracy of 96.86%, precision of 98.65%, recall of 94.99%, and an F1-score of 96.79%. This model outperforms others in stroke disease classification, demonstrating that the integration of oversampling techniques can effectively enhance prediction accuracy. Future research could focus on implementing deep learning-based models to further optimize stroke classification in the case of imbalanced data. These advancements are expected to enhance model performance, leading to a more effective and efficient approach for medical datasets.
Explainable Artificial Intelligence based Deep Learning for Retinal Disease Detection Sureja, Nitesh; Parikh, Vruti; Rathod, Ajaysinh; Patel, Priya; Patel, Hemant; Sureja, Heli
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.717

Abstract

This research focuses on the automated identification of retinal diseases. To address this challenge, an artificial intelligence-based approach developed utilizing five deep learning models namely Xception, InceptionV4, EfficientNet-B4, SqueezeNet, and ResNet-264. The model leverages transfer learning to enhance its performance. It is trained on a dataset of optical coherence tomography (OCT) images to classify retinal conditions into four categories: (1) diabetic macular edema, (2) choroidal neovascularization, (3) drusen, and (4) normal. The training dataset, sourced from publicly available repositories, comprises 1,08,312 OCT retinal images covering all four categories. The proposed models achieved good results. InceptionV4 outperformed other models across multiple metrics, achieving the highest accuracy (99.50%), precision (100%), recall (100%), AUC (100%), and F1 score (100%). It surpassed SqueezeNet (accuracy: 98.00%, precision: 98.00%, recall: 98.00%), EfficientNet-B4 (accuracy: 98.50%, precision: 98.50%, recall: 98.50%), Xception (accuracy: 78.25%, precision: 80.36%, recall: 77.75%, F1 score: 99.50%), and ResNet-264 (accuracy: 87.75%, precision: 87.94%, recall: 87.50%, F1 score: 87.98%). The results highlight the effectiveness of deep learning models combined with transfer learning in achieving accurate and efficient retinal disease detection. Future research could focus on expanding the dataset and exploring hybrid architectures to enhance classification accuracy and improve generalization across various retinal conditions
Quantum-Enhanced Brain Tumor Detection and Progression Prediction Using MRI Imaging Gangappa, Malige; Manju, D; Krishnna, Maringanti Gopi; Reddy, M. Sree Mithra; Sathish, M.; Shahabaaz, Sk; Shanthan, A.; Chaitanya, M.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.720

Abstract

Brain tumor identification and change over time analysis are essential for timely diagnosis and effective treatment scheduling and planing. This study presents a hybrid quantum-classical deep learning framework integrating Quantum Convolutional Neural Networks (QCNNs) with classical CNN to improve MRI-based tumor classification. Unlike traditional CNNs, which suffer from high computational costs and limited feature extraction capabilities, the proposed Quantum-Enhanced Tumor Analysis Framework (QETAF) leverages quantum feature maps to enhance tumor localization and segmentation. This study utilizes the BraTS MRI dataset (comprising 67,000 labeled scans) and applies contrast enhancement, intensity normalization, and augmentation techniques for preprocessing. The novel hybrid model employs CNN model for extracting the essential features initially and QCNN for refined feature representation, significantly improving tumor classification accuracy. Moreover, morphological variations can be monitored using Recurrent Quantum Neural Networks (RQNNs), which have been employed to track tumor progression. According to investigational results, RQNN increases the accuracy of tumor progress prediction, whereas QCNN beats regular CNNs with an 89% Dice Coefficient. Compared to classical models, the proposed approach reduces inference time by 28% while maintaining superior classification performance. This quantum-assisted model presents a novel pathway for enhancing computational efficiency and precision in brain tumor diagnostics, covering the way for more consistent clinical diagnostics.
Deep Learning Approach for Segmenting Nuchal Translucency Region in Fetal Ultrasound Images for Detecting Down Syndrome using GoogLeNet and AlexNet Aher, Sandip Rajendra; Agarkar, Balasaheb Shrirangrao; Chaudhari, Sachin Vasant
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.729

Abstract

Down syndrome (DS) is a chromosomal disorder linked to intellectual impairment and developmental delays in babies. The primary prenatal indicator for detecting DS during the initial stages of gestation is the thickness of nuchal translucency (NT). This paper introduces a GoogLeNet model based on convolutional neural networks (CNN) for the semantic segmentation of the NT region from ultrasound fetal images, facilitating rapid and cost-effective diagnosis in the early stages of the gestational period. A transfer learning methodology with AlexNet is employed to train the NT regions for the detection of DS. The Inception module of GoogLeNet enables the model to simultaneously capture characteristics at various sizes of images. The capacity to extract both intricate and broad characteristics can improve the model’s performance in precisely identifying the NT area. This will function as an exceptional tool for physicians in screening of DS, enhancing the detection rate and providing a substantial opinion for early diagnosis. The proposed deep learning approach attained an accuracy of 96.18% and Jaccard index of 0.967 for NT region segmentation utilizing GoogLeNet. A confusion matrix was used to evaluate the image classification by AlexNet model's effectiveness, and the results showed an overall accuracy of 97.84%, ROC-AUC of 98.45%, recall of 99.64%, precision of 96.04%, and F1 score of 97.80%. The proposed deep learning method produced remarkable outcomes and can be applied to the identification of DS in medical field. This method identifies individuals at increased risk for this condition and enables termination in the early stages of pregnancy.
Liver Cirrhosis Classification using Extreme Gradient Boosting Classifier and Harris Hawk Optimization as Hyperparameter Tuning Nalasari , Lista Tri; Anam, Syaiful; Shofianah, Nur
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.730

Abstract

This study proposes an early diagnosis model based on Machine Learning for liver cirrhosis classification using the Hepatitis C dataset, which is the leading cause of cirrhosis, from UCI ML. The classification is performed using the XGBoost algorithm because it provides high accuracy and time efficiency based on previous studies. However, these advantages depend on the combination of its hyperparameters set. XGBoost has a large number of hyperparameters, which can be time-consuming for researchers to manually configure. Therefore, this study proposes combining XGBoost with the Harris Hawks Optimization (HHO) algorithm for hyperparameter tuning. HHO is implemented with a hawk population of 40 and maximum iterations set at 25. The proposed XGBoost-HHO model provides an average performance of 99.34% for accuracy, MAR, MAP and 99.33% for Macro F1-score. These performances are achieved with the shortest processing time across 25 experiments compared to other combination models. The performance of the XGBoost-HHO model shows more significant increase in performance and reduction in overfitting compared to the standard XGBoost, SVM, RF models, as well as several other combined models including RF-HHO, SVM-HHO, XGBoost-PSO, and XGBoost-BA. Additionally, based on the feature importance analysis of the XGBoost-HHO algorithm, Alanine Aminotransferase (ALT), Protein, and Gamma-glutamyltransferase (GGT) contribute the most to the classification process, with gain values of 11.21, 9.51, and 7.98, respectively. Overall, the findings of this study show that the XGBoost-HHO algorithm combination provides competitive performance and can serve as an excellent alternative for liver cirrhosis classification in terms of both accuracy and time efficiency.

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