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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 25 Documents
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Search results for , issue "Vol 8 No 1 (2026): January" : 25 Documents clear
Classification of Ultrasound Images Using ResNet-50 with a Convolutional Block Attention Module (CBAM) Afif, Bagus Tegar Zahir; Wiharto, Wiharto; Salamah, Umi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1406

Abstract

Liver fibrosis staging is a crucial component in the clinical management of chronic liver disease because it directly affects prognosis, therapeutic decision-making, and long-term patient monitoring. Ultrasound imaging is widely used as a noninvasive diagnostic modality due to its safety, low cost, and broad accessibility. Nevertheless, ultrasound-based fibrosis assessment remains challenging because liver parenchymal echotexture often exhibits low contrast, speckle noise, and subtle inter-stage variations, particularly among adjacent METAVIR stages. These characteristics frequently limit the effectiveness of conventional convolutional neural networks, which tend to emphasize dominant global patterns while suppressing weak but clinically meaningful texture cues. This study presents a task-oriented integration of a Convolutional Block Attention Module into a ResNet-50 backbone to enhance feature discrimination for five-stage liver fibrosis classification using heterogeneous B-mode ultrasound images. Rather than introducing a new attention mechanism, the contribution lies in the systematic insertion of CBAM after residual outputs across multiple network stages, enabling repeated channel and spatial recalibration from low-level texture descriptors to higher-level semantic representations. To further improve robustness and reduce prediction variance, a stratified 5-fold training strategy is combined with logit-level ensemble inference, where logits from independently trained fold models are averaged prior to Softmax normalization. Experiments were conducted on a publicly available dataset comprising 6,323 ultrasound images acquired from two tertiary hospitals using multiple ultrasound systems, with fibrosis stages labeled from F0 to F4 according to histopathology-based METAVIR scoring. The proposed framework achieves a test accuracy of 98.34%and consistently high precision, recall, and F1 scores across all fibrosis stages, with the most pronounced improvement observed for intermediate stages. Statistical analysis based on paired fold-wise comparisons confirms that the performance gain over the baseline ResNet 50 model is statistically significant. These results demonstrate that combining lightweight attention-based feature refinement with logit ensemble inference effectively addresses the inherent challenges of ultrasound-based liver fibrosis staging and provides a reliable noninvasive decision support framework with strong potential for clinical application and future multicenter validation.
A Comparative Analysis of SMOTE and ADASYN for Cervical Cancer Detection using XGBoost with MICE Imputation Ramadhan, Mita Azzahra; Saragih, Triando Hamonangan; Kartini, Dwi; Muliadi, Muliadi; Mazdadi, Muhammad Itqan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1415

Abstract

Cervical cancer remains a significant global health burden for women, with approximately 660,000 new cases and 350,000 associated deaths recorded worldwide in 2022. Machine learning methods have shown great promise in advancing timely detection and accurate diagnosis. This investigation compares two widely used oversampling strategies, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), applied to cervical cancer identification via the XGBoost classifier, paired with Multiple Imputation by Chained Equations (MICE) to handle incomplete data. The dataset consists of cervical cancer risk factors with four diagnostic outcomes: Hinselmann, Schiller, Cytology, and Biopsy, which are treated as independent binary classification tasks rather than a single multilabel classification problem. The process began by preparing a dataset of cervical cancer risk factors through MICE imputation, then applying SMOTE and ADASYN to address class imbalance. The XGBoost model is optimized using Random Search hyperparameter tuning and evaluated across train-test split ratios (50:50, 60:40, 70:30, 80:20, and 90:10) using accuracy, precision (macro, micro, weighted), recall (macro, micro, weighted), F1-score (macro, micro, weighted), and AUC metrics. The results indicated that the XGBoost setup with MICE and SMOTE outperformed the others, achieving 97.1% accuracy, 97.1% mic-precision, 97.1% mic-recall, 97.1% mic-F1, and 97.1% AUC. Meanwhile, the ADASYN-integrated model showed marginally lower results, with 95.4% accuracy, 95.4% micro-precision, 95.4% micro-recall, 95.4% micro-F1, and 55.5% AUC. SMOTE proved more adept at creating evenly distributed synthetic data for the underrepresented group. Overall, this work underscores the value of integrating MICE imputation, SMOTE oversampling, and tuned XGBoost as a reliable approach for cervical cancer detection. These insights pave the way for automated screening tools that can bolster clinical judgment and improve early diagnosis outcomes.
Dengue Risk Stratification in Semarang City Using a Gaussian Mixture Model Based on Multi-Dimensional Urban Indicators Izzatil Ismah, Nabila; Fahmi, Amiq
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1426

Abstract

Dengue fever remains a pressing public health challenge in major Indonesian cities, including Semarang. The complex interplay of heterogeneous demographic structures and built-environment characteristics generates spatially uneven transmission risks, while conventional risk-mapping approaches often fail to capture the probabilistic nature of these risks at fine-scale administrative levels, limiting their utility for targeted interventions. This study aims to develop a robust, replicable framework for dengue risk stratification that more accurately identifies localized high-risk areas and supports evidence-based public health decision-making. The research introduces a probabilistic clustering approach using Gaussian Mixture Models (GMM) to move beyond rigid partitioning methods, while simultaneously integrating multi-year incidence data (2021–2024) with eighteen multidimensional urban indicators across 177 sub-districts (kelurahan). This combined contribution advances methodological rigor by accommodating overlapping data distributions and probabilistic cluster memberships, and provides a nuanced, evidence-driven tool for stratifying dengue risk and guiding hyper-local interventions. Several GMM configurations were evaluated using the Bayesian Information Criterion (BIC) to determine the optimal number of clusters. The BIC value declined markedly when the number of clusters increased from two to three, indicating a substantial improvement in model fit. Further increases yielded only marginal gains, and the lowest BIC was achieved at three clusters, representing the most parsimonious and effective solution. Internal validation confirmed that the cluster structure robustly captured epidemiological variance despite the inherent heterogeneity of urban spatial data. Cluster 2 emerged as a critical high-risk epicenter, geographically limited yet characterized by consistently elevated incidence, pronounced temporal variability, and extreme values. The proposed GMM-based framework demonstrates that dengue risk in Semarang is concentrated within localized foci of heightened vulnerability rather than uniformly distributed. Ultimately, the methodology is replicable in other complex tropical urban environments, thereby strengthening both academic rigor and practical public health decision-making
Mental Health Detection Expert System Model Based on DASS-42 Using Fuzzy Inference System Rahmat, Eko Ginanjar Basuki; Wiharto, Wiharto; Salamah, Umi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1443

Abstract

Mental health disorders such as depression, anxiety, and stress frequently co-occur and exhibit overlapping symptoms, making accurate diagnosis challenging due to the subjective nature of psychological assessments. Conventional use of the Depression Anxiety Stress Scales (DASS-42) relies on rigid score aggregation, while many machine learning approaches fail to adequately represent uncertainty and expert reasoning. This study aims to develop an expert system for mental health detection by integrating fuzzy logic with expert knowledge derived from the DASS-42 instrument. The main contribution of this research is a hybrid knowledge-based framework that combines decision tree–based rule extraction with psychological expert validation, ensuring both interpretability and clinical relevance. The proposed method employs a Fuzzy Inference System (FIS) using triangular and trapezoidal membership functions to model symptom intensity as linguistic variables, followed by rule generation using the CART decision tree algorithm and expert refinement. System performance is evaluated using Cohen’s Kappa coefficient, including standard error and 95% confidence intervals, to measure inter-rater reliability between the expert system, the DASS instrument, and two human experts. The results indicate that the expert system achieves almost perfect agreement in identifying dominant psychological conditions, with an average Kappa value of 0.918. For severity-level classification, strong agreement is observed for depression (Kappa = 0.842) and stress (Kappa = 0.811), while anxiety severity shows moderate-to-substantial agreement (Kappa = 0.648), reflecting inherent variability in expert interpretation. In conclusion, the proposed FIS-based expert system effectively captures expert diagnostic reasoning and outperforms decision tree–only models, demonstrating strong potential as an interpretable and reliable mental health screening tool.
Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda, Melinda; Gazali, Syahrul; Away, Yuwaldi; Rafiki, Aufa; Wong, W.K; Muliyadi, Muliyadi; Rusdiana, Siti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.

Page 3 of 3 | Total Record : 25

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