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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 12 Documents
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Search results for , issue "Vol. 8 No. 1 (2026): February" : 12 Documents clear
Design of a Clinical CRM Model Integrated with EMR to Support NCD Education Wijaya, Avid; Soultoni Akbar, Prima; Rohmania Zein, Eiska; Fadly, Fery; Ajeng Trikusumah, Rizka
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.280

Abstract

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.
Generating Synthetic B-Mode Fetal Ultrasound Images Using CycleGAN-Based Deep Learning Hermawati, Fajar Astuti; Hardiansyah, Bagus; Andrianto, Andrianto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.282

Abstract

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.
Telemedicine and AI in Remote Prediabetes Monitoring Among Adolescents Solechah, Siti Aisyah; Saputro, Setyo Wahyu; Adini, Muhammad Hifdzi; Faisal, Mohammad Reza; Kurniawan, Erick; Umiatin, Umiatin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.283

Abstract

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.
Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction Rahmayanti, Rahmayanti; Herteno, Rudy; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Abadi, Friska
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.294

Abstract

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.
Real-Time, Multi-Command Drone Navigation Using a Consumer-Grade EEG-Based SSVEP BCI Wijaya, Anderias Eko; Nurizati, Nurizati; Hermawan, Rian; Suhendra, Muhammad Agung
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.295

Abstract

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.
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation Fathur Rahman, Imam; Melinda, Melinda; Yunidar, Yunidar; Basir, Nurlida; Rafiki, Aufa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.313

Abstract

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
“Influence of Visual Input and Surface Stability on Gastrocnemius Muscle Activation During Quiet Standing Using Multi-Feature EMG and Bilateral Assessment.” Saftari, Liana Nafisa; Susanti, Hesty; Randa, Gloria Belinda; Saputra, Latifa Majesta; Azzahra, Ashila Ghaitsa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.297

Abstract

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
The Evolution of Weather-Based Deep Learning in Smart Irrigation: A Systematic Review of Sustainable Approaches and Perspectives Rahayu, Andri Ulus Rahayu; Linawati, Linawati; Sastra, Nyoman Purta; Ida Bagus Gede Manuaba
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.285

Abstract

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.
Development of a Portable  Ultrasonic Digital Anthropometry System with  Automated CIAF Classification SISWATI, TRI; Rialihanto, Muhammad Primiaji; Paramashanti, Bunga Astria; Ardiyanto, Farit; Attawet, Jutharat
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.299

Abstract

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.
Multi-Modal Ensemble Framework for Mental Health Disorder Prediction: A Novel Machine Learning Approach M. Fadli Ridhani; Tesdiq Prigel Kaloka; Yazid Aufar; Rizqiana, Annisa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.300

Abstract

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.

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