Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
The Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) 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 but not limited to: (1) Electronics, (2) Biomedical Engineering, and (3) Medical Informatics. Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers. We intend to maintain, improve the high standards of excellence, visibility, and further development of the journal. The goal is to be indexed in the Scopus and the Web of Science in the next year, with the continuous increasing the quality of the journal articles and visibility of the journal. A submitted paper should be an original work, and should not have been presented or published previously. Submission for publication in this journal presumes that the paper is not being considered for publication elsewhere and is not under review by any other publication. All articles undergo a thorough evaluation process while following all ethical guidelines to maintain the highest publishing standards. The IJEEEMI team strictly reviews the suitability, originality of manuscripts for publication.
Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia
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Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications
Software Defect Prediction (SDP) plays a strategic role in identifying software defects during the early stages of development, thereby enabling more efficient allocation of testing resources, particularly in the rapidly evolving mobile application domain characterized by fast release cycles. The commonly used Within-Project Defect Prediction (WPDP) approach is often constrained by the limited availability of historical data, especially in projects at early stages of development. As an alternative, Cross-Project Defect Prediction (CPDP) leverages historical data from other projects as training sources. Moreover, the performance of the Deep Neural Network (DNN) used in SDP is highly dependent on accurate hyperparameter configurations, where manual tuning requires substantial time and computational resources without guaranteeing optimal results. To address this issue, this study analyzes and compares the effectiveness of three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), in optimizing DNN hyperparameters within a CPDP framework. This study utilizes 14 open-source Android mobile application projects and employs the Leave-One-Out Cross-Validation technique. The performance of each combination is evaluated using ROC-AUC as the primary metric. The Wilcoxon Signed-Rank Test with a Bonferroni correction is used to assess the statistical significance of the observed performance differences. The experimental results demonstrate that GWO-DNN achieves the best performance, with an average ROC-AUC of 0.721, and is the only combination that remains statistically significant after Bonferroni correction, with a small effect size based on Cliff’s delta. Overall, the findings of this study indicate that metaheuristic-based hyperparameter tuning is a sufficiently effective approach for improving the capability of DNN in cross-project software defect prediction within the mobile application domain, although the observed improvements remain moderate.
Copyright (c) 2026 Maulana Abdul Rahman, Rudy Herteno, Radityo Adi Nugroho, Friska Abadi, Setyo Wahyu Saputro (Author) https://creativecommons.org/licenses/by-sa/4.0
Determinants of User Satisfaction and Net Benefits of Electronic Medical Record Information Systems in Eastern Military Hospitals
The rapid digital transformation in healthcare has accelerated the adoption of Electronic Medical Records (EMR) to improve service quality, data management, and patient safety. However, despite national policies mandating EMR implementation in Indonesia, adoption remains uneven across healthcare facilities, particularly in regions with limited digital infrastructure and technological readiness. Hospitals in eastern Indonesia face challenges such as unstable internet connectivity, limited technological resources, and varying levels of digital literacy, which may reduce the effectiveness and benefits of EMR implementation. This study aims to analyze the influence of system quality, information quality, and service quality on user satisfaction and the net benefits of EMR implementation using the DeLone and McLean Information System Success Model. A quantitative cross-sectional design was employed using survey data collected from 84 EMR users at Tk. III J.A. Dimara Hospital, Manokwari. Total sampling was applied to include all active EMR users who met the inclusion criteria. Data were analyzed using SmartPLS 4.0 software with Partial Least Squares Structural Equation Modeling (PLS-SEM). Statistical significance was determined using a two-tailed test with a critical t-value > 1.96 and p < 0.05 at the 95% confidence level. The results indicate moderate explanatory power, with R² values of 0.534 for system use, 0.638 for user satisfaction, and 0.578 for net benefits. Seven out of nine hypotheses were supported. System quality and information quality significantly influence system use, while information quality and service quality significantly influence user satisfaction. However, system quality does not significantly affect user satisfaction, and service quality does not significantly affect system use. Furthermore, system use and user satisfaction significantly contribute to net benefits, with user satisfaction demonstrating the strongest effect. These findings suggest that improving information quality, service support, and effective system utilization is essential for enhancing EMR performance and achieving meaningful organizational benefits in hospital settings
Copyright (c) 2026 Aldy Fitrah Bramantio (Author) https://creativecommons.org/licenses/by-sa/4.0
Data Splitting Strategies for Down Syndrome Facial Classification: A Comparative Study Using EfficientNet-B0 and MobileNetV2
Early identification of Down Syndrome (DS) is essential for timely intervention; however, conventional diagnostic approaches often require specialized clinical expertise and significant resources. Recent advances in deep learning-based facial image analysis offer a promising alternative, yet the impact of data partitioning strategies on model performance and stability remains insufficiently explored. This study investigates the effects of different data-splitting strategies on DS facial image classification using EfficientNet-B0. A total of 3,030 facial images were collected from Roboflow and curated through preprocessing techniques, including Gaussian noise reduction, image sharpening, and contrast enhancement. Two data partitioning configurations, 70:20:10 and 80:10:10, were evaluated using five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score, while statistical significance was examined using the Friedman test. The results show that the 70:20:10 configuration achieved an average accuracy of 87.88% ± 3.03%, while the 80:10:10 configuration achieved a slightly higher accuracy of 89.09% ± 2.53%. The Friedman test indicates statistically significant differences (p < 0.05). However, the improvement is relatively marginal, with a small-to-moderate effect size (Cohen’s d = 0.43) and no significant difference in variance (p > 0.05), indicating limited practical significance. A trade-off between accuracy and evaluation stability was observed. While the 80:10:10 configuration benefits from a larger training set, the 70:20:10 configuration provides more stable and balanced performance, particularly in minimizing false negatives. These findings highlight that higher accuracy does not necessarily imply more reliable or clinically meaningful performance, emphasizing the importance of appropriate data partitioning in medical image classification.
Copyright (c) 2026 Dzaki Dhiya Ul-Haq, Yunidar Yunidar, Melinda Melinda, Nurlida Basir, Rosmawinda Rosmawinda (Author) https://creativecommons.org/licenses/by-sa/4.0
Python-Based Backend Architecture Design for Commercial Medical IoT Device Integration: A Case Study of Omron HEM-7142T1
The current implementation of Remote Patient Monitoring (RPM) still faces crucial challenges related to the accuracy and integrity of medical data. Many healthcare IoT devices rely on generic sensors that require rigorous manual calibration and exhibit unstable error rates, failing to meet international clinical standards. This study aims to design and implement an integrated backend architecture that bridges certified commercial medical devices with digital health systems. The main contribution is a six-layer IoT architecture specifically designed to integrate the Omron HEM-7142T1 device to ensure data validity in remote blood pressure monitoring. Following the Design Science Research Methodology (DSRM), the system was developed using Python, the Bleak library for Bluetooth Low Energy (BLE) communication, and FastAPI to provide interoperable REST API services. Functional testing in Postman demonstrated that the system successfully extracts medical data, producing JSON output with an HTTP 200 OK status under single-access conditions. However, load testing using Apache JMeter with 10 virtual users revealed limitations in the hardware’s point-to-point BLE protocol. The /scan endpoint showed stable performance with a 0% error rate and an average response time of 5.04 seconds. In contrast, endpoints /connect-and-read and endpoint /latest-bp-records recorded error rates of 100% and 90%, respectively, with an average response time of 23.29 seconds when accessed simultaneously, due to the Omron device’s locking mechanism. This study concludes that while the six-layer architecture effectively ensures medical data integrity in single-access scenarios, it requires a database caching module in the Logic Tier to overcome parallel access constraints. The implementation provides a foundation for developing secure, standardized professional RPM systems for medical use.
Copyright (c) 2026 Tien Fabrianti Kusumasari, Yudhi Widyatama, Alaric Rasendriya Aniko, Sinung Suakanto (Author) https://creativecommons.org/licenses/by-sa/4.0
Advancing PSA Maturity Level 4 Through a Web-Based PHP–MySQL Predictive Dashboard for Hospital Utilities and Medical Gases
Public hospitals in Indonesia operate under the Public Service Agency (PSA/BLU) governance framework, which requires balanced clinical performance and financial accountability. Indicator 6.2 of the PSA Maturity Rating mandates the transition from fragmented manual reporting toward systematic and predictive digital monitoring to achieve Level 4 (“Predictable”) governance. However, many institutions continue to rely on retrospective reporting systems that impede transparency and data-driven decision-making. This study aims to develop and validate a web-based predictive dashboard to strengthen resource governance at Dr. M. Djamil Central General Hospital (RSUP Dr. M. Djamil Padang), Indonesia. The system integrates four critical resource streams electricity, water, fuel, and medical gases using a bounded Annual Growth Rate (±20%) model combined with a deviation-adjusted hybrid forecasting approach and a Sugeno-type Fuzzy Inference System for priority classification. A two-year longitudinal validation (2024–2025) was conducted using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The results demonstrate high predictive stability, with a weighted-average MAPE below 10%, and electricity forecasts classified as “Highly Accurate.” Water and Liquid O2 emerged as high-priority operational pressures, while other parameters remained within controlled growth thresholds. The proposed framework operationalizes Indicator 6.2 by institutionalizing a transparent and reproducible predictive monitoring mechanism. This study contributes a scalable digital governance prototype for emerging healthcare institutions seeking to advance toward Predictable maturity while strengthening risk-informed resource allocation.
Fuzzy Inference System; Governance; Maturity Rating; Predictive Dashboard; Public Service Agency
Copyright (c) 2026 Catur Suharinto (Author) https://creativecommons.org/licenses/by-sa/4.0
A Comparative Analysis of Lightweight Deep Learning Models for CT-Based Kidney Disease Classification to Support Early Detection in Geriatric Care
Kidney diseases, including cysts, stones, and tumors, are common among older adults and often progress asymptomatically, leading to delayed diagnoses. Manual interpretation of CT images by clinicians is labor-intensive and can vary significantly between observers, especially in high-volume settings. This study aims to develop and evaluate an artificial intelligence–based decision support system for multiclass kidney disease classification with an emphasis on robustness, computational efficiency, and clinical feasibility in elderly healthcare environments. The study proposes a medical informatics evaluation framework that integrates standard performance metrics with learning dynamics, overfitting analysis, and error distribution assessments to ensure reliable model selection. Three architectures were evaluated: a conventional CNN, MobileNet-V2, and EfficientNet-B0. Experiments were conducted on a publicly available dataset containing 12,446 CT images across four classes (Normal, Cyst, Stone, and Tumor). Models were trained under varying epoch settings and evaluated using weighted accuracy, precision, recall, F1-score, AUC, learning curve analysis, and confusion matrix assessment. The results indicate that the conventional CNN achieved perfect numerical performance but exhibited rapid convergence and early metric saturation, limiting the interpretability of generalization under the current dataset configuration. EfficientNet-B0 showed stable yet conservative performance, whereas MobileNet-V2 achieved near-optimal accuracy with gradual convergence, minimal overfitting, and superior computational efficiency. At the optimal configuration (epoch 50), MobileNet-V2 achieved an accuracy of 1.00, precision of 1.00, recall of 1.00, F1-score of 1.00, and an AUC of 0.9997. These findings suggest that lightweight architectures, particularly MobileNet-V2, offer a practical solution for CT-based kidney disease decision support, while acknowledging the need for patient-level and multi-institutional validation.
Copyright (c) 2026 Ardha Ardhana Putra Agustavada, Aji Prasetya Wibawa, Abdullah Sholum, Dafa Fadhilah Hilmi, Felix Andika Dwiyanto (Author) https://creativecommons.org/licenses/by-sa/4.0
The Effect of Smote-Tomek on the Classification of Chronic Diseases Based on Health and Lifestyle Data
Machine learning models for chronic disease prediction are often trained on imbalanced healthcare datasets, where non-disease cases dominate. This condition can lead to misleadingly high accuracy while failing to identify patients with chronic diseases, limiting clinical usefulness. This study aims to analyze the impact of class imbalance on model performance and to evaluate the effectiveness of the SMOTE–Tomek resampling technique in improving chronic disease prediction. This research provides empirical evidence that accuracy alone is insufficient for evaluating healthcare models and demonstrates that imbalance-aware preprocessing is essential for valid and reliable chronic disease detection. Five classification models, such as Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost, were evaluated on a lifestyle-based chronic disease dataset under two conditions: without resampling and with SMOTE–Tomek. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC. Without SMOTE–Tomek, all models failed to detect chronic disease cases, producing near-zero recall and F1-scores despite accuracy exceeding 80%. After applying SMOTE–Tomek, substantial improvements were observed across all models, particularly in recall and AUC. Support Vector Machine achieved the best overall performance, with an accuracy of 92.9%, a precision of 92%, a recall of 93.9%, an F1-score of 0.93, and an AUC of 0.98. The findings confirm that handling class imbalance is a prerequisite for meaningful chronic disease prediction. The consistent increase in recall and AUC across all evaluated models confirms that the improvement stems from enhanced class separability rather than metric inflation. The proposed approach supports more reliable early screening and decision-support systems in preventive healthcare
Copyright (c) 2025 Muhammad Adika Riswanda, Friska Abadi, Muhammad Itqan Mazdadi, Mohammad Reza Faisal, Rudy Herteno (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparison Between K-Fold Cross Validation And Percentage Split In Decision Tree Algorithms For Anemia Classification
Anemia is a significant global health challenge characterized by a pathological deficit in hemoglobin concentration, often leading to physiological instability. Accurate clinical diagnosis typically relies on complete blood count (CBC) tests, which provide critical hematological parameters for classification. While machine learning models have demonstrated high efficacy in diagnosing anemia, existing research often relies on static data partitioning strategies that may overlook evaluation reliability and performance stability. This study addresses this gap by shifting the focus from architectural benchmarking to validation robustness, specifically evaluating the C4.5 algorithm's performance across different data-splitting techniques. The research uses a dataset comprising 1,281 clinical records with 14 numerical features and 9 anemia-type labels. To assess stability, two distinct partitioning strategies were implemented: a static Percentage Split (ranging from 60:40 to 90:10) and iterative K-Fold Cross Validation (with K values of 3, 5, 7, 10, and 15). Experimental results demonstrate that the C4.5 algorithm achieved its peak performance with the 90:10 Percentage Split, achieving an average accuracy of 99.46%, precision of 98.32%, and recall of 99.28%. In comparison, the K-Fold (K=10) approach yielded a slightly lower but more stable accuracy of 99.19% with a significantly reduced standard deviation (±0.09), highlighting its reliability for clinical applications. While the high-ratio percentage split maximizes training exposure and predictive potential, the K-Fold method provides a more objective, generalizable benchmark by accounting for the entire data distribution. The study further identifies challenges in classifying minority classes, such as Leukemia with thrombocytopenia, due to inherent data scarcity. Ultimately, this research confirms that the C4.5 algorithm, when paired with an optimal partitioning protocol, remains a robust and highly interpretable solution for clinical anemia screening, outperforming several complex modern architectures
Copyright (c) 2025 Nanda Putri Rahmawati, Irwan Budiman, Muhammad Itqan Mazdadi, Andi Farmadi, Friska Abadi (Author) https://creativecommons.org/licenses/by-sa/4.0
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation
Accurate and reliable classification of autism spectrum disorder (ASD) from electroencephalography (EEG) signals remains challenging due to the inherently nonstationary, nonlinear, and multichannel nature of EEG data. These properties complicate the extraction of discriminative features that are both stable and computationally efficient. To address this challenge, this study proposes a compact deep-learning pipeline that integrates the Multivariate Empirical Wavelet Transform (MEWT) with EEGNet for ASD–EEG classification. MEWT decomposes multichannel EEG signals into spectrally aligned subbands while preserving inter-channel relationships. The resulting MEWT-based features are then processed by EEGNet, a lightweight convolutional neural network specifically designed for EEG-based learning tasks. Performance was evaluated using 5-fold cross-validation. The proposed MEWT with the the EEGNet model achieved a mean test accuracy of 98.35%, with consistently high precision (98.23%), recall (98.45%), F1-score (98.34%), and specificity (98.24%) across all folds. Confusion-matrix results indicated very few and well-balanced false positives and false negatives, supporting stable discrimination between ASD and control EEG segments. A one-sample one-tailed t-test against a 50% chance level confirmed that all evaluated metrics were significantly above chance (p < 0.0001). When benchmarked against previously reported results on the same dataset, the proposed approach slightly improved upon EMD with EEGNet (97.99%) and clearly outperformed EWT with EEGNet (95.08%), suggesting that MEWT-derived multichannel features are well matched to compact convolutional architectures for ASD–EEG analysis. Despite these strong results, the study is limited by a small, single-site cohort and the use of a single deep-learning model. Future work will focus on standardized retraining across multiple feature families and validation on larger and more diverse populations to further assess robustness and generalizability
Multivariate Empirical Wavelet Transform
Copyright (c) 2025 Imam Fathur Rahman, Melinda Melinda, Yunidar Yunidar, Nurlida Basir, Aufa Rafiki (Author) https://creativecommons.org/licenses/by-sa/4.0
A Two-Stage Hybrid Oversampling and Ensemble Learning Framework for Improved Type 2 Diabetes Mellitus Classification
Type 2 Diabetes Mellitus (T2DM) screening using clinical tabular data commonly suffers from class imbalance, where non-diabetic records dominate diabetic cases, causing models to bias toward the majority class and yield poor detection of the positive (diabetic) class. This study aims to improve T2DM classification on an imbalanced dataset by increasing minority-class detection while maintaining acceptable overall performance. The main contribution is a leakage-safe framework that integrates two-stage hybrid oversampling (RandomOverSampler followed by Borderline-SMOTE) and soft-voting ensemble learning to obtain more balanced predictions. Experiments were conducted on the Diabetes Bangladesh (DiaBD) dataset, containing 5,288 clinical records with a binary target, diabetic (Yes/No, mapped to 1/0). The data were stratified into train_full/test splits (80/20) and further into train/validation splits (80/20 of train_full). Features were normalized using MinMaxScaler fitted only on the training set and applied to validation and test sets to prevent data leakage. Class imbalance handling was applied only on the training set using the proposed two-stage oversampling (ROS Borderline-SMOTE; borderline-1, k_neighbors=3). Classification models included SVM (RBF), Random Forest, and Gradient Boosting, as well as soft-voting ensembles of two and three models. Results show that the baseline setting (No OS) can achieve high accuracy but low minority detection; for instance, SVM (No OS) reached an accuracy of 0.9374 with a Recall_pos of 0.0909 and an F1_pos of 0.1587. After oversampling, SVM (OS) improved minority recall to 0.7273 with F1_pos 0.4188, although accuracy decreased to 0.8688 due to increased false positives. The best-balanced performance was achieved by the SVM + RandomForest soft-voting ensemble (OS) with accuracy 0.9125, Recall_pos 0.6545, and the highest F1_pos 0.4932. Overall, the proposed two-stage hybrid oversampling combined with soft-voting ensembles improves T2DM detection on imbalanced tabular data, and the findings highlight that model selection should prioritize Recall_pos and F1_pos rather than accuracy alone.
Copyright (c) 2026 Siti Fatimah Nurdiah Permatasari, Ermatita Ermatita (Author) https://creativecommons.org/licenses/by-sa/4.0
Multi-Modal Ensemble Framework for Mental Health Disorder Prediction: A Novel Machine Learning Approach
Mental health disorders constitute a major global public health concern, affecting millions of individuals across diverse socioeconomic and cultural contexts. Accurate prediction of mental health outcomes at the population level remains challenging due to the complex and non-linear relationships among co-occurring disorders. Previous studies relying on traditional statistical approaches, particularly linear regression, have reported limited predictive performance, with an R² of approximately 0.7175. This limitation highlights the need for more advanced analytical frameworks capable of capturing comorbidity patterns and non-linear interactions among mental health conditions. This study proposes and evaluates a novel multi-modal ensemble machine learning framework to improve the prediction accuracy of eating disorder prevalence using global mental health data. The analysis utilizes country-level prevalence data for schizophrenia, depression, anxiety, bipolar disorder, and eating disorders across multiple countries and years. Eating disorder prevalence is modeled as the primary target variable, while other mental health disorders are incorporated as predictive features to represent clinically established comorbidity relationships. To enhance the representational capacity of the data, an extensive feature engineering strategy was applied, generating 19 additional features through polynomial transformations, interaction terms, ratio-based indicators, and aggregate burden measures. Unsupervised clustering techniques, including K-Means, DBSCAN, and hierarchical clustering, were employed to identify natural groupings of countries based on their mental health profiles. Furthermore, ten machine learning algorithms were systematically evaluated, including linear models, tree-based methods, neural networks, and support vector regression. The best-performing models were subsequently integrated into a stacking ensemble architecture. Experimental results demonstrate that the proposed stacking ensemble achieved a test R² score of 0.9955, corresponding to a 42.2% improvement over the baseline linear regression model. These results indicate that multi-modal ensemble approaches substantially enhance predictive accuracy and provide valuable insights to support evidence-based global mental health policy and targeted intervention planning.
Copyright (c) 2025 M. Fadli Ridhani, Tesdiq Prigel Kaloka, Yazid Aufar, Annisa Rizqiana (Author) https://creativecommons.org/licenses/by-sa/4.0
Development of a Portable Ultrasonic Digital Anthropometry System with Automated CIAF Classification
Child malnutrition, particularly stunting, remains a critical public health issue with long-term effects on growth and cognitive development. In many community health settings, conventional anthropometric measurement tools used in community health services often present challenges, including human measurement error, non-standard data recording, and a lack of real-time diagnostic output. This study aims to develop and validate a portable ultrasonic sensor-based digital anthropometric system capable of automatically detecting the Composite Index of Anthropometric Failure (CIAF) in real-time. The novelty of this research lies in integrating non-contact ultrasonic height measurement with automated CIAF classification based on WHO 2006 growth standards, cloud-connected data storage, and a user-friendly interface designed for community health workers. This Research and Development study involved system design, laboratory calibration, field validation, and user acceptability testing. A total of 80 toddlers and 30 users (midwives, nutritionists, and Posyandu cadres) participated across regions with low and high stunting prevalence. Measurement accuracy was compared to gold-standard anthropometry, while usability was assessed through a Likert-scale evaluation. Laboratory tests indicated measurement error ranging from 0.0 to 0.2 cm, indicating high sensor precision. Field tests showed a mean difference of ≤1 cm with no statistically significant difference (p>0.05) compared to standard measurement. User evaluation reported high satisfaction, particularly in ease of use (92%), accuracy (90%), and program support benefit (94%). The developed portable ultrasonic digital anthropometry system provides accurate, fast, and standardized CIAF-based malnutrition detection, supporting more efficient child growth monitoring programs. The tool demonstrates strong potential for integration into community-based nutrition surveillance and national health information systems.
Copyright (c) 2025 TRI SISWATI, Muhammad Primiaji Rialihanto, Bunga Astria Paramashanti, Farit Ardiyanto, Jutharat Attawet (Author) https://creativecommons.org/licenses/by-sa/4.0
“Influence of Visual Input and Surface Stability on Gastrocnemius Muscle Activation During Quiet Standing Using Multi-Feature EMG and Bilateral Assessment.”
Postural stability depends on multisensory integration, yet most studies focus on a single EMG feature or sensory condition at a time. This creates a significant gap in understanding how multiple EMG features change when various sensory inputs are altered during quiet standing. To address this, the present study examined bilateral medial and lateral gastrocnemius activation using five EMG features: Mean Absolute Value (MAV), Root Mean Square (RMS), Waveform Length (WL), Integrated EMG (IEMG), and Total Power (PT) across four sensory conditions that combine visual input (eyes open or closed) and surface stability (stable or unstable). A one-way ANOVA revealed significant condition effects for RMS, MAV, WL, and IEMG (p < 0.05), while PT showed only a non-significant trend. Paired t-test results indicated that MAV significantly increased on the unstable surface with eyes closed compared to the stable surface (t(4) = 4.793, p = 0.009), WL increased in the right lateral gastrocnemius under the same condition (t(4) = 3.976, p = 0.016), and closing the eyes on a stable surface significantly increased WL in the right medial gastrocnemius (t(4) = 6.209, p = 0.003). Across features, the right gastrocnemius consistently showed greater modulation than the left, suggesting dominance-related asymmetry in neuromuscular control. This study provides one of the first bilateral multi-feature EMG characterizations of sensory perturbations during quiet standing. The findings demonstrate that the absence of vision increases neuromuscular demand even on stable surfaces, and that unstable surfaces further amplify activation, particularly in complexity-related features such as WL. These outcomes highlight the potential of EMG features, especially WL, as objective biomarkers for balance assessment. Clinically, the results may inform rehabilitation and fall-prevention programs by supporting the use of unstable surfaces and vision-restricted exercises to enhance proprioceptive and vestibular compensation
Copyright (c) 2025 Liana Nafisa Saftari, Hesty Susanti, Gloria Belinda Randa, Latifa Majesta Saputra, Ashila Ghaitsa Azzahra (Author) https://creativecommons.org/licenses/by-sa/4.0
Real-Time, Multi-Command Drone Navigation Using a Consumer-Grade EEG-Based SSVEP BCI
Steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) provide a non-invasive method for hands-free device control. However, their practical applications are limited by reliance on costly laboratory-grade electroencephalography (EEG) systems. This study addresses this gap by designing and evaluating a real-time, six-command SSVEP-BCI for drone navigation using a consumer-grade EEG headset. An adaptive processing pipeline was developed to extract spectral and spatial features, which were classified using Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models. Analysis of data from 30 participants revealed that the RF classifier achieved an optimal balance between performance and speed, with a high classification accuracy of 87.24% and a low computational latency of 0.09 seconds, resulting in a high information transfer rate (ITR) of 35.0 bits/min. In contrast, the ANN was insufficiently accurate, and SVM performance was marginal. These findings demonstrate the viability of low-cost, multi-command SSVEP-BCIs for applications in assistive technology, teleoperation, and human-computer interaction.
Copyright (c) 2025 Anderias Eko Wijaya, Nurizati Nurizati, Rian Hermawan, Muhammad Agung Suhendra (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction
Software defect prediction (SDP) is essential for improving software reliability by enabling the early identification of modules that may contain defects before the release stage. SDP commonly exhibits redundant or non-contributory metrics, underscoring the need for feature selection to derive a more informative subset. To address this problem, the present study investigates and compares the effectiveness of three feature-selection strategies: SelectKBest (SKB), Recursive Feature Elimination (RFE), and the hybrid SKB+RFE, in enhancing the performance of tree-based classifiers on the NASA Metrics Data Program (MDP) data collections. The study utilizes three classification algorithms, namely Random Forest (RF), Extra Trees (ET), and Bagging (Decision Tree), with Area Under the Curve (AUC) serving as the primary metric for assessing model performance. Experimental results reveal that the RFE and Extra Trees combination yields the top performance, producing an average AUC of 0.7855. This is subsequently followed by the SKB+RFE+ET configuration, which achieves an AUC of 0.7809, and SKB+ET at 0.7776. These findings demonstrate that iterative wrapper-based approaches such as RFE can identify more relevant and effective feature subsets than filter or hybrid strategies, with the RFE+Extra Trees configuration yielding the strongest overall predictive performance and wrapper-based methods exhibiting higher stability across heterogeneous datasets. Even without hyperparameter tuning and relying solely on class-weighting rather than explicit resampling techniques, the findings offer empirical insight into the isolated influence of feature selection on predictive performance. Overall, the study confirms that RFE combined with Extra Trees offers the strongest predictive performance on NASA MDP data collections and forms a foundation for developing more adaptive and robust models.
Copyright (c) 2025 Rahmayanti Rahmayanti, Rudy Herteno, Setyo Wahyu Saputro, Triando Hamonangan Saragih, Friska Abadi (Author) https://creativecommons.org/licenses/by-sa/4.0
Improved Glucose Detection in Urine Using a Silver-Based Plasmonic Sensor with Ni and TiO2 Thin Films in Kretschmann Configuration
Surface plasmon resonance (SPR) sensors have emerged as powerful tools for label-free and real-time detection of biomolecules, yet their performance often depends on optimizing the sensing structure. This work proposes and analyzes a silver-based SPR sensor in the Kretschmann configuration, enhanced with thin layers of Ni and TiO2, for non-invasive glucose detection in urine. The optical response was calculated using the transfer matrix method, while sensitivity and figure of merit (FOM) were employed as performance indicators. The incorporation of ultrathin Ni and TiO2 layers atop the silver film induces a pronounced shift in the SPR toward higher incidence angles, accompanied by an enhanced angular response to refractive index variations, thereby indicating improved sensitivity. However, further increases in the thickness of these layers lead to an excessive redshift of the resonance dip toward the extreme angular range, reducing resonance definition and ultimately limiting the sensor's detection capability. The results show that the introduction of Ni and TiO2 significantly improves both sensitivity and spectral sharpness, with the optimal BK7/Ag(30 nm)/Ni(14 nm)/TiO2(5 nm) structure achieving a maximum sensitivity of 432 deg/RIU. This configuration detected glucose concentrations as low as 0.625 g/dL, corresponding to a refractive index change of only 0.001 RIU. Finite-difference time-domain analysis further confirmed that the performance enhancement originates from stronger localization of the electric field at the TiO2 interface, as evidenced by the presence of the Ni layer. These findings demonstrate the effectiveness of Ni and TiO2 layers in enhancing plasmonic responses and highlight the strong potential of this sensor design for practical biomedical applications. In particular, the proposed structure offers a promising pathway for developing simple, non-invasive urine-based glucose monitoring systems.
Copyright (c) 2026 Yuant Tiandho, Erna Julianti, Zamziri Zamziri, Putri Cahyani, Fitri Afriani (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimization of Intelligent Grid Strength and Grid Ratio to Improve Pelvic Radiographic Image Quality in Digital Radiography
Pelvic radiography is an essential diagnostic procedure; however, its image quality is often degraded by scattered radiation, which reduces contrast and diagnostic accuracy. Conventional physical grids can reduce scatter but have limitations, including increased patient dose and workflow constraints, particularly in mobile radiography. The Intelligent Grid, a digital scatter correction technology, provides a promising alternative, yet the optimal combination of grid ratio and strength parameters remains unclear. This study aims to optimize the strength and grid ratio of the Intelligent Grid to improve pelvic radiographic image quality. This study introduces a novel quantitative optimization approach that systematically integrates grid ratio and strength variations within the Intelligent Grid system to enhance pelvic radiographic image quality in digital radiography. A quantitative experimental approach with a post-test only design was conducted using a mobile X-ray system, digital radiography, an Aero DR detector, and pelvic phantoms. Data were acquired by performing anteroposterior pelvic radiography exposures (60 kV & 10 mAs) with the Intelligent Grid. Strength variations (slightly strong, normal, slightly weak) and grid ratios (3:1, 6:1, 8:1, 10:1, 12:1) were applied to generate different parameter combinations. Image quality was evaluated quantitatively (histogram and SNR) and qualitatively through visual grading by three radiologists. Statistical analysis was performed using Repeated ANOVA and Friedman tests. Statistical analysis showed significant differences (p < 0.001) in pelvic radiographic image quality across Intelligent Grid ratio and strength combinations. The 12:1 grid ratio with slightly strong strength yielded the highest mean SNR, balanced grayscale distribution, and top visual grading scores, confirming its optimal performance in enhancing image contrast and anatomical visibility of the iliac contour, acetabulum, and pubic symphysis. In conclusion, optimal adjustment of Intelligent Grid strength and grid ratio parameters can substantially improve pelvic radiographic image quality, providing a practical alternative to conventional physical grids in digital radiography
Copyright (c) 2026 Fani Susanto, Hernastiti Sedya Utami, Kusnanto Mukti Wibowo, Samudra Prihatin Hendra Basuki, Widya Mufida, Nurul Fadhlina Binti Ismail (Author) https://creativecommons.org/licenses/by-sa/4.0
Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network
Rice diseases such as leaf blight, blast, and brown spot remain major constraints on food security and rural livelihoods across Southeast Asia, causing significant yield losses each year. In Indonesia, particularly in Lamongan, East Java, these pathogens threaten smallholder productivity and disrupt national rice supply chains. This study aims to enhance automated rice disease detection under real agricultural conditions by integrating image preprocessing techniques with a deep learning-based detection framework. The main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training, effectively mitigating low-contrast conditions and noise commonly found in field-acquired image datasets. The enhanced images are subsequently processed using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a ResNet-50 backbone to localize and classify disease symptoms. Experiments conducted on a dataset of 1,500 annotated rice leaf images achieved high detection performance, with accuracies of 97.37% for leaf blight, 94.12% for blast, and 95.24% for brown spot. Compared with the baseline Faster R-CNN model, the proposed approach improved classification accuracy from 0.8906 to 0.9297, reduced false negatives from 0.439 to 0.1998, increased foreground classification accuracy from 0.55 to 0.78, and descreased total loss from 0.839 to 0.6493. These results demonstrate that integrating RGB-to-grayscale conversion and contrast stretching significantly enhances feature representation, leading to improved detection accuracy, reduced error rates, and more stable training behavior. Overall, the proposed framework provides a robust and reliable approach for rice disease identification and offers strong potential for practical deployment in precision agriculture systems.
Copyright (c) 2026 Monika Faswia Fahmi, Deni Tri Laksono, Achmad Fiqhi Ibadillah, Dedi Tri Laksono (Author) https://creativecommons.org/licenses/by-sa/4.0
The Evolution of Weather-Based Deep Learning in Smart Irrigation: A Systematic Review of Sustainable Approaches and Perspectives
This paper presents a systematic literature review of 191 peer-reviewed studies that link short-term weather information with learning based forecasting and control in irrigation or related applications, focusing on 191 peer-reviewed studies published between January 2020 and early 2025, with four foundational studies published prior to 2020 included via backward citation tracking. The review follows a PRISMA-inspired protocol, with database searches in Scopus, IEEE Xplore, and Web of Science, clear inclusion and exclusion criteria, and structured data extraction on the application domain, sensing and IoT architecture, forecasting models, reinforcement learning algorithms, and reported performance metrics. The results show that deep learning models, especially CNN, LSTM, and their hybrids, are frequently used for short-term environmental prediction and typically outperform classical machine learning baselines. Almost 50 studies employ reinforcement learning or deep reinforcement learning, but only five (≈2.6% of the full corpus) apply these methods directly to irrigation control, while most DRL applications appear in energy and smart-grid management. Around a quarter of the corpus explicitly implements IoT architectures, yet very few systems integrate IoT with reinforcement learning in a closed loop at the edge or fog. Sustainability-related outcomes, such as water use, energy savings, costs, and emissions, are mentioned, but they are not consistently quantified using comparable metrics. The review provides a structured mapping of methods and architectures, clarifies how existing work is fragmented across domains, and highlights open opportunities for developing weather-aware, IoT-enabled, and sustainability-oriented reinforcement-learning frameworks for smart irrigation.
Copyright (c) 2025 Andri Ulus Rahayu Rahayu, Linawati Linawati, Nyoman Purta Satra, Ida Bagus Gede Manuaba (Author) https://creativecommons.org/licenses/by-sa/4.0
EEG-Based Analysis of Concentration with Shooting Accuracy and Precision in Archery Athletes: A Quantitative Correlational Study
Archery performance as a precision sport is determined by a complex interaction between psychological and physiological factors. Concentration, as a crucial factor, can be objectively measured through electroencephalography (EEG) by detecting beta waves. However, current coaching practices disproportionately emphasize physical aspects while systematically ignoring concentration as a crucial psychological factor. Generally, studies assess performance through aggregate scores without distinguishing between two fundamental dimensions: accuracy and precision. This study aims to analyze the relationship between concentration levels measured through beta band power activity using EEG and shooting performance in archery athletes, focusing on shooting accuracy and shooting precision. This study offers empirical contributions about the relationship between concentration and two dimensions of shooting performance, develops a methodological validation that integrates EEG monitoring with smart bow technology, and establishes a practical foundation for developing concentration-based training programs in archery. The research subjects consisted of 12 novice archery athletes. EEG data were acquired with electrodes positioned at AF7 and AF8, monitoring beta band power during shot execution. Pearson’s correlation was used to analyze the relationship. The results showed a shot accuracy with an average score of 131.17, while precision showed an average SRD of 11.00 cm. Beta band power had a mean of 39.18 µV². Correlation analysis revealed a non-significant positive relationship between beta power and accuracy (r = 0.145, p = 0.652), as well as a non-significant negative relationship with precision (r = -0.327, p = 0.300). Study findings show that beta wave activity alone does not serve as a significant predictor of shooting performance in novice archers. However, the differential correlation pattern (positive for accuracy, negative for precision) confirms that these two dimensions are influenced by different psychophysiological mechanisms.
Copyright (c) 2026 Ainun Rahmansyah Gaffar, Pringgo Widyo Laksono, Bambang Suhardi, Rahmaniyah Dwi Astuti, Minoru Sasaki (Author) https://creativecommons.org/licenses/by-sa/4.0
Telemedicine and AI in Remote Prediabetes Monitoring Among Adolescents
The escalating prevalence of prediabetes in Indonesia, particularly among children and adolescents, necessitates the development of lightweight, adaptable, and cost-effective telemedicine solutions for the noninvasive monitoring of blood glucose levels. Existing systems predominantly employ machine learning and deep learning approaches that require substantial computational resources and stable internet connectivity, limiting their applicability in regions with constrained digital infrastructure. The objective of this study is to develop an artificial intelligence (AI)–driven telemedicine system that employs an expert system to determine prediabetes status by utilizing commercially available smartwatches as noninvasive optical sensors. The methodological approach includes an examination of smartwatch capabilities to identify Bluetooth Low Energy (BLE) sensors, service architectures, and the Generic Attribute Profile (GATT); the development of a Rule-Based Reasoning (RBR) expert system to determine prediabetes status using Fasting Plasma Glucose (FPG) and Postprandial Plasma Glucose (PP2) measurements; and the application of Rapid Application Development (RAD) methods in the development of Flutter-based mobile applications and Laravel Inertia Vue–based web applications. The results of this study demonstrate that the telemedicine system operates in both offline and online modes and incorporates AI functionality on mobile devices and servers without requiring extensive computational resources. All system functionalities successfully passed testing, and the expert system achieved 100% accuracy in determining prediabetes status. In conclusion, the integration of telemedicine and AI-based expert systems provides an effective, economical, and flexible solution that can be widely implemented in Indonesia to reduce the increasing incidence of prediabetes through continuous digital health monitoring.
Copyright (c) 2025 Siti Aisyah Solechah, Setyo Wahyu Saputro, Muhammad Hifdzi Adini, Mohammad Reza Faisal, Erick Kurniawan, Umiatin Umiatin (Author) https://creativecommons.org/licenses/by-sa/4.0
Generating Synthetic B-Mode Fetal Ultrasound Images Using CycleGAN-Based Deep Learning
B-mode ultrasound (USG) is a key imaging modality for fetal assessment, providing a noninvasive approach to monitor anatomical development and detect congenital anomalies at an early stage. However, portable ultrasound devices commonly used in low-resource healthcare settings often yield low-resolution images with significant speckle noise, reducing diagnostic accuracy. Furthermore, the scarcity of labeled medical data, caused by privacy regulations such as HIPAA and the high cost of expert annotation, poses a significant challenge for developing robust artificial intelligence (AI) diagnostic models. This study proposes a CycleGAN-based deep learning model enhanced with a histogram-guided discriminator (HisDis) to generate realistic synthetic B-mode fetal ultrasound images. A publicly available dataset from the Zenodo repository containing 1,000 grayscale fetal head images was utilized. Preprocessing included normalization, histogram equalization, and image resizing, while the architecture combined two ResNet-based generators and a dual discriminator configuration integrating PatchGAN and histogram-guided evaluation. The model was trained using standard optimization settings to ensure stable convergence. Experimental results demonstrate that the proposed HisDis module accelerates convergence by 18 epochs and reduces the Fréchet Inception Distance (FID) by 23.6 percent from 1580.72 to 1208.49 compared with the baseline CycleGAN. Statistical analysis revealed consistent pixel-intensity distributions between the original and synthetic images, with entropy from 7.16 to 7.40. At the same time, visual assessment confirmed that critical anatomical structures, including the brain midline and lateral ventricles, were well preserved. These results indicate that the CycleGAN-HisDis model produces statistically and visually realistic fetal ultrasound images suitable for medical data augmentation and AI-based diagnostic training. Furthermore, this approach holds potential to enhance diagnostic reliability and clinical education in healthcare settings with limited imaging resources. Future work will focus on clinical validation and generalization across diverse fetal ultrasound datasets.
Copyright (c) 2025 Fajar Astuti Hermawati, Bagus Hardiansyah, Andrianto Andrianto (Author) https://creativecommons.org/licenses/by-sa/4.0
Design of a Clinical CRM Model Integrated with EMR to Support NCD Education
Non-Communicable Diseases (NCDs) have become a major global health issue requiring serious attention, a challenge for the global health system, because of factors such as urbanization, lifestyle changes, and population aging have caused increased cases of diseases such as diabetes, cancer, and cardiovascular disorders worldwide. Clinics play a crucial role in providing health education to the community to increase awareness of NCD prevention. Health service optimization can be achieved through the integration of Customer Relationship Management (CRM) and Electronic Medical Record (EMR) systems, which enable improved patient-provider relationships and more effective access to medical records. This study aims to develop an integrated CRM EMR application model for clinics as a tool for NCD education. The study employed a user-centered design approach with a descriptive research methodology. The research stages consisted of research, requirements, design, and evaluation, conducted through literature reviews, field data collection, and interviews with clinic staff to identify system requirements and user experiences. The design phase produced a database model, relational tables, and a user interface prototype that represent the integration of CRM and EMR systems. The findings indicate that the proposed model has the potential to improve the efficiency of patient data management, facilitate health education, and strengthen interactions between patients and healthcare providers. The main contribution of this research is the provision of technical outputs in the form of a database design and user interface prototype, which can serve as the foundation for further development and implementation of an integrated CRM EMR system in clinics. By embedding the CRM–EMR model within broader e-health strategies and community-based health promotion programs, this research lays the groundwork for a sustainable digital ecosystem that supports preventive healthcare and advances Indonesia’s digital health transformation.
Copyright (c) 2025 Avid Wijaya, Prima Soultoni Akbar, Eiska Rohmania Zein, Fery Fadly, Rizka Ajeng Trikusumah (Author) https://creativecommons.org/licenses/by-sa/4.0
Topic Mining-Based Knowledge Discovery of User Health Information Needs
Understanding the user’s need for health information has become increasingly important as the use of digital health services continues to grow. However, the unstructured data of user-generated questions presents challenges in accurately capturing and analyzing these needs. This study contributes to addressing SDG 3 (Good Health and Well-being) by utilizing topic mining-based knowledge discovery to identify the primary topics emerging from user questions submitted through the “Tanya Dokter” feature on the Alodokter platform. A total of 8,550 questions were obtained through web scraping between July 2024 and June 2025. The collected data were preprocessed and subsequently analyzed using seven topic modeling approaches: Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Latent Semantic Analysis (LSA), Non-negative Matrix Factorization (NMF), BERTopic, Top2Vec, and ProdLDA. To assess model performance, the coherence metric (c_v) was employed to identify the most effective method. Among these techniques, NMF achieved the best results, producing the highest coherence score of 0.67 with six well-defined topics. The findings show six primary areas of concern: pregnancy; menstruation and contraceptive management; general health and minor ailments; infant care; dermatological conditions; and musculoskeletal and other physical complaints. General health-related issues occurred most frequently, particularly during seasonal transitions, while menstruation and contraceptive management received the least attention, despite menstruation contributing to women’s health risks and the use of contraceptives helping to reduce maternal mortality in Indonesia. These findings offer valuable insights for digital health platforms like Alodokter to enhance information delivery and health literacy, ultimately improving online health services and supporting the achievement of SDG 3
Copyright (c) 2025 Dayana Khoiriyah Harahap, Ken Ditha Tania, Putri Eka Sevtiyuni (Author) https://creativecommons.org/licenses/by-sa/4.0
CoAtNet for Chest X-Ray Report Generation with Bi-LSTM and Multi-Head Attention
In clinical environments, Chest X-Ray (CXR) represents the most prevalent diagnostic instrument, particularly facilitating diagnostic procedures through medical report. However, manual report preparation is time-consuming, highly dependent on the expertise of radiologists, and carries the risk of errors due to high workloads and limited expert staff. Therefore, an automated system based on artificial intelligence is needed to ease the workload of radiologists while increasing consistency. This study aims to develop an automated medical report generation system with balanced data distribution, reliable encoder, and bidirectional contextual understanding. The main contributions of this study include the implementation of an undersampling strategy based on majority captions followed by oversampling on minority labels while maintaining a proportion of labels with higher frequencies, the use of Bi-LSTM with Multi Head Attention (MHA) to strengthen text context understanding, and the use of CoAtNet as a visual encoder that combines the strengths of CNN and Transformer. The methodology incorporates image preprocessing via gamma correction for contrast improvement, data selection, balancing through combined undersampling and oversampling, and CoAtNet implementation as encoder paired with Bi-LSTM and MHA as decoder. Experimental execution employed the IU X-ray dataset, with assessment conducted using BLEU and ROUGE-L metrics. Outcomes revealed that the CoAtNet configuration with Bi-LSTM and MHA, coupled with the undersampling-oversampling strategy, delivered superior performance evidenced by a cumulative score of 1.642, with BLEU-1 to BLEU-4 and ROUGE-L achieving 0.480, 0.329, 0.245, 0.183, and 0.405, respectively. These findings prove that the combination of data balancing strategies with CoAtNet and Bi-LSTM is able to produce more accurate automated medical reports and reduce bias towards the majority label.
Copyright (c) 2025 Rafy Aulia Akbar, Ricky Eka Putra, Wiyli Yustanti (Author) https://creativecommons.org/licenses/by-sa/4.0
Gait Variability and Phase Segmentation in Obese and Normal Individuals Using Multi-Location IMUs and Hidden Markov Models Supervised Marginal
Obesity is known to disrupt motor control and biomechanics; however, detailed gait alterations in individuals with obesity remain underexplored, particularly in dynamic and real-world walking conditions. This study aims to quantitatively characterize gait differences between individuals with obesity and those of normal weight by analyzing postural and temporal gait parameters. The investigation focuses on pitch, roll, and cadence dynamics using body-worn inertial sensors, with phase transition modeling via Hidden Markov Models. This work proposes a novel framework that integrates multi-location Inertial Measurement Unit (IMU) sensors and a Hidden Markov Model–Supervised Marginal (HMM-SM) approach to detect and classify gait phases with high accuracy, offering practical value for clinical gait assessment and personalized rehabilitation. IMU sensors were placed on the waist, thigh, calf, and heel to record gait data from participants in both obese and normal-weight groups. Gait segmentation and phase modeling were conducted using 4-, 5-, and 8-state HMMs. Quantitative analysis revealed significantly greater postural variability in the obese group during slow walking, with standard deviations in roll and pitch reaching 20.68° and 9.23°, respectively—much higher than the normal-weight group (0.60° and 0.26°). Hidden state transitions from 5-state pitch HMMs showed a very strong effect size for the obese group (Cramér’s V = 0.72) compared to a moderate effect for the normal-weight group (V = 0.33). Similar patterns were observed for roll and cadence. In terms of segmentation accuracy, the 4- and 5-state HMMs outperformed the 8-state model, achieving accuracy levels above 99%, while the 8-state model reached only ~93%. The findings demonstrate that obesity significantly alters gait dynamics, particularly in postural stability and gait phase transitions. The proposed IMU-based HMM-SM framework effectively captures these changes, offering a reliable tool for gait analysis in clinical and biomechanical applications.
Copyright (c) 2025 Suto Setiyadi, Husneni Mukhtar , Willy Anugrah Cahyadi, Diyah Widiyasari, Mohamad Ramdhani, Nigel Bryan Tang (Author) https://creativecommons.org/licenses/by-sa/4.0
Hyperparameter optimization of breast ultrasound image classification models using ant colony optimization based on texture features
Breast cancer is one of the most prevalent types of cancer in humans and has the highest cumulative risk compared to other types of cancer. Accurate diagnosis and efficient intervention for this disease are very important to improve patient survival. This study aims to optimize machine learning algorithms using a limited number of features in order to produce an efficient breast cancer classification model that remains competitive with deep learning–based models. Furthermore, this study is expected to assist pathologists and doctors in the treatment of breast cancer. The dataset used in this study consists of three classes (benign, malignant, and normal) with a total of 780 breast ultrasound images from 600 patients. All images were processed and augmented to enrich data variation before modeling using five classification algorithms: Random Forest, SVM, Decision Tree, Gradient Boosting, and k-NN. Modeling was conducted in two scenarios: without optimization and with hyperparameter optimization using the Ant Colony Optimization algorithm. The results showed that the GLCM angle orientation had a relatively small effect on model performance. The best accuracy for each orientation was achieved with k-NN+ ACO (0.95 at 00), SVM+ACO (0.94 at 450), SVM+ACO (0.90 at 900), and RF+ACO (0.95 at 1350).
Copyright (c) 2025 Ahmad Fauzi, Lukmanda Evan Lubis, Raju Wandira, Syarto Musthofa (Author) https://creativecommons.org/licenses/by-sa/4.0
Deep Learning-Based Hippocampal Segmentation and MTA Classification Using U-Net with ResNet-50 Backbone
Medial Temporal Atrophy (MTA) is a key biomarker in the early diagnosis of dementia. However, its assessment through manual inspection of MRI scans is subjective, time-consuming, and prone to inter-observer variability. This creates the need for automated systems that can provide accurate, consistent, and clinically interpretable evaluations. This study aims to develop a hybrid deep learning framework that integrates U-Net with a ResNet-50 backbone for simultaneous hippocampal segmentation and MTA grading, thereby reducing diagnostic subjectivity and bridging the gap between image processing and clinical interpretation. The main contribution of this work lies in the dual functionality of the proposed architecture: not only producing precise segmentation masks of the hippocampal region but also classifying the degree of atrophy into MTA scores (0–4), which previous studies on hippocampal segmentation have not addressed. The proposed method employs a U-Net for pixel-level segmentation, enhanced with a ResNet-50 backbone to stabilize gradient propagation and enrich feature representation during encoding. Results demonstrated excellent performance, achieving a training accuracy of 99.9% with strong convergence between training and validation curves. On a test set of 32 coronal MRI slices, the model correctly classified 26 samples, misclassifying only 6. Overall, the proposed U-Net with ResNet-50 backbone provides an accurate and reliable end-to-end solution for hippocampal segmentation and MTA grading. Its clinical performance demonstrates parity with expert radiologists, underscoring its potential as a decision-support tool in dementia diagnosis. Future work will focus on extending this framework to 3D U-Net architectures, enabling the integration of volumetric MRI features to enhance robustness and generalizability across diverse datasets further
Copyright (c) 2025 Aldienannisa Devin Salsabila, Fatimah Fatimah, Darmini Darmini, Selamet Budi Kurniawan (Author) https://creativecommons.org/licenses/by-sa/4.0
Design of Instrument Measurement for X-Ray Radiation with Geiger Muller
Radiation monitoring aims to know firsthand the rate of radiation exposure in a work area to ensure the safety and health of workers who will work in the radiation emitting area in accordance with the principle of ALARA (As Low As Reasonably Achievable). This study developed a nature X-ray radiation measuring device using a Geiger Muller tube detector and can display the results of the measurement of numbers in microSievert units and Counter Per Minute to the LCD Character display and Android which have function to reduce the radiation exposure received by the radiation workers. The output of the detector is processed using Arduino Uno. Comparison of the results of the module with a calibrated standard survey meter measures the reference that the module can be used. The radiation detection system testing of this module is carried out to adjust the current condition of the Covid-19 pandemic, so that the module tests the background radiation (natural radiation). Based on module testing and experiments, it was obtained that data from 10 times data collection showed the accuracy value of the radiation measuring device using a Geiger Muller detector was 90.71% for the measurement of background radiation in a closed room. The Geiger Muller detector is not accurate for measuring small radiation exposures, the module can be used to measure background radiation and fluoroscopy X-ray radiation
X-Ray; Geiger Muller; Arduino Uno, Android;
Copyright (c) 2020 https://creativecommons.org/licenses/by-sa/4.0
Modification of Infant Warmer with PID Temperature Controlled with Apgar Monitoring and Respiration Rate
Newborns, both healthy and premature babies, have a difficult problem, newborns withstood the environment with new ones that previously depended on the mother's uterus and also as a place for initial examination in nayi after just being discussed. The purpose of this study was to add a baby warmer with PID temperature control and add respiration parameters and APGAR. The contribution in this study is the PID control system which is used to control temperature and is also a sensor of respiration rates to determine respiration in infants. So that the values ​​of the temperature sensor and Respiration Rate sensor can be stable it is necessary to adjust the sensor's reading time. The LM35 sensor can activate temperature in the body, the Flex Sensor is used to read respiration values ​​in infants. Based on data collection of respiration rates taken from adults, an average value of 18.5 times per minute was obtained in the first attempt, and 21 times per minute in the second trial. Respiratory rate values ​​that have not been stable take data because there are still external factors, such as changes from respondents and others. The results of this research can be implemented on baby warmers to improve the application of updates to the baby.
Infant warmer, temperature, PID, Respiration Rate