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INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 7 Documents
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Search results for , issue "Vol. 8 No. 2 (2026): May" : 7 Documents clear
EEG-Based Analysis of Concentration with Shooting Accuracy and Precision in Archery Athletes: A Quantitative Correlational Study Ainun Rahmansyah Gaffar; Pringgo Widyo Laksono; Bambang Suhardi; Rahmaniyah Dwi Astuti; Minoru Sasaki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.284

Abstract

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.
A Comparative Analysis of Lightweight Deep Learning Models for CT-Based Kidney Disease Classification to Support Early Detection in Geriatric Care Ardha Ardhana Putra Agustavada; Aji Prasetya Wibawa; Abdullah Sholum; Dafa Fadhilah Hilmi; Felix Andika Dwiyanto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.325

Abstract

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.
Python-Based Backend Architecture Design for Commercial Medical IoT Device Integration: A Case Study of Omron HEM-7142T1 Kusumasari, Tien Fabrianti; Widyatama, Yudhi; Aniko, Alaric Rasendriya; Suakanto, Sinung
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.331

Abstract

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.
A Two-Stage Hybrid Oversampling and Ensemble Learning Framework for Improved Type 2 Diabetes Mellitus Classification Permatasari, Siti Fatimah Nurdiah; Ermatita, Ermatita
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.308

Abstract

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.
Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network Fahmi, Monika Faswia; Laksono, Deni Tri; Ibadillah, Achmad Fiqhi; Laksono, Dedi Tri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.287

Abstract

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.
Advancing PSA Maturity Level 4 Through a Web-Based PHP–MySQL Predictive Dashboard for Hospital Utilities and Medical Gases Suharinto, Catur
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.329

Abstract

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.
Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications Abdul Rahman, Maulana; Herteno, Rudy; Adi Nugroho, Radityo; Abadi, Friska; Wahyu Saputro, Setyo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.340

Abstract

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.

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