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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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Computational Analysis of Medical Image Generation Using Generative Adversarial Networks (GANs) Shrina Patel; Makwana, Ashwin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.784

Abstract

The limited availability of diverse, high-quality medical images constitutes a significant obstacle to training reliable deep-learning models that can be used in clinical settings. The traditional methods used for data augmentation generate inadequate medical photos that result in poor model performance and a low rate of successful generalization. This research studies the effectiveness of DCGAN cGAN CycleGAN and SRGAN GAN architectures through performance testing in five essential medical imaging datasets, including Diabetic Retinopathy, Pneumonia and Brain Tumor and Skin Cancer and Leukemia. The main achievement of this research was to perform an extensive evaluation of these GAN models through three key metrics: generation results, training loss metrics, and computational resource utilization. DCGAN generated stable high-quality synthetic images, whereas its generator produced losses from 0.59 (Pneumonia) to 6.24 (Skin Cancer), and its discriminator output losses between 0.29 and 6.25. CycleGAN showed the best convergence potential for Diabetic Retinopathy with generator and discriminator losses of 2.403 and 2.02 and Leukemia with losses at 3.325 and 3.129. The SRGAN network produced high-definition images at a generator loss of 6.253 and discriminator loss of 6.119 for the Skin Cancer dataset. Still, it failed to maintain crucial medical characteristics in grayscale images. GCN exhibited stable performance across all loss metrics and datasets. The DCGAN model required the lowest computing resources for 4 to 7 hours, using 0.9M and 1.4M parameters. The framework of SRGAN consumed between 7 and 10 hours and needed 1.7M to 2.3M parameters for its operation, and CycleGAN required identical computational resources. DCGAN was determined as the ideal model for synthetic medical image generation since it presented an optimal combination of quality output and resource efficiency. The research indicates that using DCGAN-generated images to increase medical datasets serves as a solution for boosting AI-based diagnostic system capabilities within healthcare.
Predicting Construction Costs with Machine Learning: A Comparative Study on Ensemble and Linear Models Chen, Lifei; Tiang, Sew Sun; Chong, Kim Soon; Sharma, Abhishek; Berghout, Tarek; Lim, Wei Hong
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.799

Abstract

Accurate prediction of construction costs plays a pivotal role in ensuring successful project delivery, influencing budget formulation, resource allocation, and financial risk management. However, traditional estimation methods often struggle to handle complex, nonlinear relationships inherent in construction datasets. This study proposes a process innovation by systematically evaluating six machine learning (ML) models, i.e., Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbors (KNN), XGBoost, and CatBoost, on a standardized RSMeans dataset comprising 4,477 real-world construction data points. The primary aim is to benchmark the predictive performance, generalizability, and stability of both linear and ensemble models in construction cost forecasting. Each model is subjected to rigorous hyperparameter tuning using grid search with 5-fold cross-validation. Performance is assessed using R² (coefficient of determination), RMSE (root mean squared error), and MBE (mean bias error), while confidence intervals are computed to quantify predictive uncertainty. Results indicate that linear models achieve modest accuracy (R² ≈ 0.83), but struggle to model nonlinear interactions. In contrast, ensemble-based models significantly outperform , i.e., XGBoost and CatBoost achieve R² values of 0.988 and 0.987, respectively, RMSE values below 0.5, and near-zero MBE. Moreover, confidence interval visualization and feature importance analysis provide transparency and interpretability, enhancing the models practical applicability. Unlike prior studies that compare models in isolation, this work introduces a unified, interpretable framework and highlights the trade-offs between accuracy, overfitting, and deployment readiness. The findings have real-world implications for contractors, project managers, and cost engineers seeking reliable, data-driven decision support systems. In summary, this study present a scalable and robust ML-based framework that facilitate process innovation in construction cost estimation, paving the way for more intelligent, efficient, and risk-aware construction project management.
Power-Efficient 8-Bit ALU Design Using Squirrel Search and Swarm Intelligence Algorithms Pasaya, Ashish; Hadia, Sarman; Bhatt, Kiritkumar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.822

Abstract

The Arithmetic Logic Unit (ALU) serves as a core digital computing element which performs arithmetic functions along with logic operations. The normal operation of ALU designs leads to increased power consumption because of signal redundancy and continuous operation when new data inputs are unavailable. The research implements the Squirrel Search Algorithm (SSA) combined with Swarm Intelligence Algorithm (SIA) for 8-bit ALU optimization to achieve maximum resource efficiency alongside computational accuracy. The optimization properties of SSA and SIA make them ideal choices for digital circuit design applications because they yielded successful results in power-aware systems. The proposed method utilizes SSA-based conditional execution paired with SIA-based transition minimization to direct operations to execute only during fluctuating input data conditions thus eliminating undesired calculations. Studies confirm SSA and SIA function more effectively than distributed clock gating for power saving because they enable runtime-dependent optimization without creating significant computational overhead. The experimental Xilinx Vivado tests executed on an AMD Spartan-7 FPGA (XC7S50FGGA484) running at 100 MHz frequency established that SSA eliminates power consumption from 6 mW to 2 mW, and SIA achieves a power level of 4 mW. The SSA algorithm generates worst negative slack (WNS) values of 8.740 ns which SIA produces as 6.531 ns improving system timing performance. SSA-optimized ALU requires the same number of LUTs as the unoptimized design at 42 LUTs yet SIA uses 50 LUTs because of added logical elements. We observe no changes in flip-flop use during SSA where nine FFs remain yet SIA shows an increase in its usage up to 29 FFs due to input tracking. The study proves that bio-inspired methods create energy-efficient platforms which make them ideal for implementing ALU designs with FPGAs. Research studies demonstrate that hybrid swarm intelligence techniques represent an unexplored potential to optimize power-efficient architectures thus reinforcing their significance for future high-performance energy-efficient digital systems.
Classification of Cervical Cell Types Based on Machine Learning Approach: A Comparative Study Wan Mustafa, Wan Azani; Khiruddin, Khalis; Jamaludin, Khairur Rijal; Khusairi, Firdaus Yuslan; Ismail, Shahrina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.829

Abstract

Cervical cancer remains a major global health issue and is the second most common cancer affecting women worldwide. Early detection is crucial for effective treatment, but remains challenging due to the asymptomatic nature of the disease and the visual complexity of cervical cell structures, which are often affected by inconsistent staining, poor contrast, and overlapping cells. This study aims to classify cervical cell images using Artificial Intelligence (AI) techniques by comparing the performance of Convolutional Neural Networks (CNNs), Support Vector Machine (SVMs), and K-Nearest Neighbors (KNNs). The Herlev Pap smear image dataset was used for experimentation. In the preprocessing phase, images were resized to 100 × 100 pixels and enhanced through grayscale conversion, Gaussian smoothing for noise reduction, contrast stretching, and intensity normalization. Segmentation was performed using region-growing and active contour methods to isolate cell nuclei accurately. All classifiers were implemented using MATLAB. Experimental results show that CNN achieved the highest performance, with an accuracy of 85%, a precision of 86.7%, and a sensitivity of 83%, outperforming both SVM and KNN. These findings indicate that CNN is the most effective approach for cervical cell classification in this study. However, limitations such as class imbalance and occasional segmentation inconsistencies impacted overall performance, particularly in detecting abnormal cells. Future work will focus on improving classification accuracy, especially for abnormal samples , by exploring data augmentation techniques such as Generative Adversarial Networks (GANs) and implementing ensemble learning strategies. Additionally, integrating the proposed system into a real-time diagnostic platform using a graphical user interface (GUI) could support clinical decision-making and enhance cervical cancer screening programs.
Addressing Intrinsic Data Characteristics Issues of Imbalance Medical Data Using Nature Inspired Percolation Clustering Siddavatam, Kaikashan; Shinde, Subhash
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.835

Abstract

Data on diseases are generally skewed towards either positive or negative cases, depending on their prevalence. The problem of imbalance can significantly impact the performance of classification models, resulting in biased predictions and reduced model accuracy for the underrepresented class. Other factors that affect the performance of classifiers include intrinsic data characteristics, such as noise, outliers, and within-class imbalance, which complicate the learning task. Contemporary imbalance handling techniques employ clustering with SMOTE (Synthetic Minority Oversampling Technique) to generate realistic synthetic data that preserves the underlying data distribution, generalizes unseen data and mitigates overfitting to noisy points. Centroid-based clustering methods (e.g., K-means) often produce synthetic samples that are too clustered or poorly spaced. At the same time, density-based methods (e.g., DBSCAN) may fail to generate sufficient meaningful synthetic samples in sparse regions. The work aims to develop nature-inspired clustering that, combined with SMOTE, generates synthetic samples that adhere to the underlying data distribution and maintain sparsity among the data points that enhance performance of classifier. We propose PC-SMOTE, which leverages Percolation Clustering (PC), a novel clustering algorithm inspired by percolation theory. The methodology of PC utilizes a connectivity-driven framework to effectively handle irregular cluster shapes, varying densities, and sparse minority instances. The experiment was designed using a hybrid approach to assess PC-SMOTE using synthetically generated data with variable spread and other parameters; second, the algorithm was evaluated on eight sets of real medical datasets. The results show that the PC-SMOTE method works excellently for the Breast cancer dataset, Parkinson's dataset, and Cervical cancer dataset, where AUC is in the range of 96% to 99%, which is high compared to the other two methods. This demonstrates the effectiveness of the PC-SMOTE algorithm in handling datasets with both low and high imbalance ratios and often demonstrates competitive or superior performance compared to K-means and DBSCAN combined with SMOTE in terms of AUC, F1-score, G-mean, and PR-AUC.
Exploring Dataset Variability in Diabetic Retinopathy Classification Using Transfer Learning Approaches Patni, Kinjal; Shruti Yagnik; Pratik Patel
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.838

Abstract

Diabetic retinopathy (DR) stands as a primary international cause of vision impairment that needs effective and swift diagnostic services to protect eye structures from advancing deterioration. The variations of imaging data that appear between sources create major obstacles for achieving consistent performance from models. The elimination of performance fluctuation problems during DR classifications across two benchmark datasets EYE-PACS and APTOS is examined through systematic transfer learning analysis using different high-performing CNN architectures including VGG16, VGG19, ResNet50, Xception, InceptionV3, MobileNetV2, and InceptionResNetV2. The research evaluates how data heterogeneity affects and how augmentation approaches impact the accuracy while stabilizing robustness in deep learning models. The research provides new insights through its extensive investigation of generalization performance based on dataset changes which utilize modified data augmentation methods for retinal images. A collection of data transformations such as rotation, flipping, zooming and brightness modifications create simulated realistic scenarios to handle imbalanced data classes. Academic research involved CNN pre-training followed by transfer learning on both databases while researchers evaluated the models through both untreated source data and augmented image testing procedures. InceptionResNetV2 outperformed its counterparts with 96.2% accuracy and Xception delivered 95.7% accuracy in APTOS evaluation and both models scored 95.9% and 95.4% respectively on EYE-PACS testing. When augmentation was applied it increased the performance level by 3% to 5% across all running models. The experimental outcomes demonstrate how adequate variable training allows these models to recognize datasets regardless of their heterogeneity. This analysis confirms that combining reliable deep learning structures with purposeful data enhancement techniques substantially enhances DR diagnosis reliability to build scalable future diagnostic solutions for ophthalmology practice.
BHMI: A Multi-Sensor Biomechanical Human Model Interface for Quantifying Ergonomic Stress in Armored Vehicle Mutiara, Giva Andriana; Adiluhung, Hardy; Periyadi, Periyadi; Alfarisi, Muhammad Rizqy; Meisaroh, Lisda
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.877

Abstract

Ergonomic stress inside armored military vehicles presents a critical yet often overlooked risk to soldier safety, operational effectiveness, and long-term health. Traditional ergonomic assessments rely heavily on subjective expert evaluations, failing to capture dynamic environmental stressors such as vibration, noise, thermal fluctuations, and gas exposure during actual field operations. This study aims to address this gap by introducing the Biomechanical Human Model Interface (BHMI), a multi-sensor platform designed to objectively quantify ergonomic stress under operational conditions. The main contribution of this work is the development and validation of BHMI, which integrates anthropometric human modeling with embedded environmental sensors, enabling real-time, multi-dimensional ergonomic data acquisition during vehicle maneuvers. BHMI was deployed in high-speed off-road vehicle operations, simulating the 50th percentile Indonesian soldier’s seated posture. The system continuously monitored vibration (0–16 g range), noise (30–130 dB range), temperature (–40°C to 80°C), humidity (0–100% RH), and gas concentration (CO and NH₃) using calibrated, field-hardened sensors. Experimental results revealed ergonomic stress levels exceeding human tolerance thresholds, including vibration peaks reaching 9.8 m/s², cabin noise levels up to 100 dB, and cabin temperatures exceeding 39°C. The use of BHMI improved the repeatability and precision of ergonomic risk assessments by 27% compared to traditional methods. Seating gap deviations of up to ±270 mm were identified when soldiers wore full operational gear, highlighting critical areas of postural fatigue risk. In conclusion, BHMI represents a novel, sensor-integrated approach to ergonomic evaluation in military environments, enabling more accurate design validation, reducing subjective bias, and providing actionable insights to enhance soldier endurance, comfort, and mission readiness.
Dual Attention and Channel Atrous Spatial Pyramid Pooling Half-UNet for Polyp Segmentation Sarira, Beatrix Datu; Prasetyo, Heri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.893

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with two million cases detected in 2020 and causing one million deaths annually. Approximately 95% of CRC cases originate from colorectal adenomatous polyps. Early detection through accurate polyp segmentation is crucial for preventing and treating CRC effectively. While colonoscopy screening remains the primary detection method, its limitations have prompted the development of Computer-Aided Diagnostic (CAD) systems enhanced by deep learning models. This study proposes a novel neural network architecture called Dual Attention and Channel Atrous Spatial Pyramid Pooling Half-UNet (DACHalf-UNet) for medical polyp image segmentation that balances optimal performance with computational efficiency. The proposed model builds upon the U-Net framework by integrating Double Squeeze-and-Excitation (DSE) blocks in the encoder after the Ghost Module, Channel Atrous Spatial Pyramid Pooling (CASPP) in the bottleneck and decoder, and Attention Gate (AG) mechanisms within the architecture. DACHalf-UNet was trained and evaluated on the CVC-ClinicDB and Kvasir-SEG datasets for 70 epochs. Evaluations demonstrated superior performance with F1-Score and IoU values of 94.23% and 89.28% on CVC-ClinicDB, and 88.40% and 81.47% on Kvasir-SEG, respectively. Comparative analysis showed that DACHalf-UNet outperforms existing architectures including U-Net, U-Net++, ResU-Net, AGU-Net, CSAP-UNet, PRCNet, UNeXt, and UNeSt. Notably, the model achieves this performance with only 0.56 million trainable parameters and 30.29 GFLOPs, significantly reducing computational complexity compared to previous methods. These results demonstrate that DACHalf-UNet effectively addresses the need for accurate and efficient polyp segmentation, potentially enhancing CAD systems and contributing to improved CRC detection and treatment outcomes.
Performance Comparison of Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (H-ELM) Methods for Heart Failure Classification on Clinical Health Datasets Ichwan Dwi Nugraha; Triando Hamonangan Saragih; Irwan Budiman; Dwi Kartini; Fatma Indriani; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.904

Abstract

Heart failure is one of the leading causes of death worldwide and requires accurate and timely diagnosis to improve patient outcomes. However, early detection remains a significant challenge due to the complexity of clinical data, high dimensionality of features, and variability in patient conditions. Traditional clinical methods often fall short in identifying subtle patterns that indicate early stages of heart failure, motivating the need for intelligent computational techniques to support diagnostic decisions. This study aims to enhance predictive modeling for heart failure classification by comparing two supervised machine learning approaches: Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (HELM). The main contribution of this research is the empirical evaluation of HELM's performance improvements over conventional ELM using 10-fold cross-validation on a publicly available clinical dataset. Unlike traditional neural networks, ELM offers fast training by randomly assigning weights and analytically computing output connections, while HELM extends this with a multi-layer structure that allows for more complex feature representation and improved generalization. Both models were assessed based on classification accuracy and Area Under the Curve (AUC), two critical metrics in medical classification tasks. The ELM model achieved an accuracy of 73.95% ± 8.07 and an AUC of 0.7614 ± 0.093, whereas the HELM model obtained a comparable accuracy of 73.55% ± 7.85 but with a higher AUC of 0.7776 ± 0.085. In several validation folds, HELM outperformed ELM, notably reaching 90% accuracy and 0.9250 AUC in specific cases. In conclusion, HELM demonstrates improved robustness and discriminatory capability in identifying heart failure cases. These findings suggest that HELM is a promising candidate for implementation in clinical decision support systems. Future research may incorporate feature selection, hyperparameter optimization, and evaluation across multi-center datasets to improve generalizability and real-world applicability.
AMIN-CNN: Enhancing Brain Tumor Segmentation through Modality-Aware Normalization and Deep Learning Depuru, Sivakumar; Kumar, M. Sunil
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.934

Abstract

Accurate segmentation of reliable brain tumor detection is essential for early diagnosis and treatment, which helps to increase patient survival rates. However, the inherent variability in tumor shape, size, and intensity across different MRI modalities makes automated segmentation a challenging task. Traditional deep learning approaches, such as U-Net and its variants, provide robust results but often struggle with modality-specific inconsistencies and generalization across diverse datasets. This research presented AMIN-CNN, an adaptive multimodal invariant normalization incorporating a novel 3D convolutional neural network to improve brain tumors segmentation across various MRI technologies. Through adaptive normalization, AMIN-CNN covers modality-specific differences more effectively than Basic CNN and U-Net, leading to improved integration of multimodal MRI input data. The model maintains strong learning performance with minimal overfitting beyond epoch 50. Regularization techniques can reduce this. AMIN-CNN stands out with the best Dice Score (about 0.92 WT, 0.87 ET, and 0.89 TC), Precision (0.3), accuracy of 93.2 % and can decrease false positives. The lower Sensitivity in AMIN-CNN results in it finding the smaller but more correct tumor regions, making it more precise. Compared with traditional methods, AMIN-CNN demonstrates a competitive or better segmentation result and maintains computational efficiency. The model has demonstrated strong independence, with a Hausdorff Distance of 20, compared to 100 for other models. According to these test results, AMIN-CNN is the most effective and clinically correct method among the different architectures, mainly due to its high precision and ability to measure tumors with accuracy.

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