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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 199 Documents
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Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization Rafiki, Aufa; Melinda, Melinda; Oktiana, Maulisa; Dewi Meutia, Ernita; Afnan, Afnan; Mulyadi, Mulyadi; Zakaria, Lailatul Qadri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/40n05b64

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behavioral patterns. Early detection of ASD is crucial for improving the quality of life of affected individuals and alleviating the burden on their families. This study proposes a computer-aided diagnostic system for ASD by applying a pre-trained Vision Transformer (ViT-B/16) architecture to EEG signal data obtained from King Abdul Aziz University. The dataset comprises EEG recordings from 16 subjects (8 normal and 8 ASD) that have undergone preprocessing—including filtering using the Discrete Wavelet Transform (DWT), segmentation (windowing), and conversion into heatmap representations—and were subsequently partitioned into training, validation, and testing subsets. The ViT model was trained for 100 epochs with a batch size of 16, using the AdamW optimizer and the CrossEntropy loss function, while two learning rate configurations (0.0001 and 0.00001) were evaluated; the best-performing weights were selected based on the lowest validation loss. Test results indicate that the model trained with a learning rate of 0.00001 achieved a testing accuracy of 99.53%, accompanied by excellent precision, specificity, recall, and f1-score, thereby demonstrating strong generalization capabilities and minimal overfitting. Future research is recommended to incorporate locally sourced datasets and to further customize the ViT architecture through comprehensive hyperparameter tuning, with the aim of developing a mobile application to support clinical ASD diagnosis.
Expert System for Early Detection of Thalassemia Disease Using Case-Based Reasoning Method Setiani, Rahma; Djatmiko, Wahyu; Kurniawan, Rozali Arsyad; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/gt9h2k22

Abstract

Thalassemia is a blood disorder characterized by abnormalities in globin chain formation. In Banyumas Regency, the prevalence of thalassemia continues to increase yearly, while detection processes are often delayed due to limited access to experts. This study aims to develop a web-based expert system for the early detection of thalassemia using the Case-Based Reasoning (CBR) method with the K-Nearest Neighbor (KNN) algorithm. The system is designed to help identify individuals who may carry the thalassemia gene trait, enabling faster and more accurate treatment. The system was tested using the black box method to ensure all features function properly across all user roles, including general users, administrators, and experts. Accuracy evaluation was conducted using a confusion matrix, achieving an accuracy rate of 95,23% based on 21 test data samples. The results indicate that this system provides highly accurate early detection and supports preventive efforts against thalassemia. Further development is recommended to create an Android-based application to enhance accessibility for the broader community. Additionally, continuous updates to the knowledge base are necessary to improve the system's accuracy and scope. This study is expected to contribute to the prevention and management of thalassemia, increase public awareness, and support better healthcare services in Indonesia
Bioelectrical Impedance Spectroscopy (BIS) For Ratiometric Identification Alhaq, Elmira Rofida; Salwa, Umaimah Mitssalia Umi; Ain, Khusnul; Sapuan, Imam
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/j81sf349

Abstract

This research explores the potential use of Electrical Impedance Spectroscopy (EIS) and ratiometric methods to improve security and reproducibility in bioelectrical impedance-based biometric authentication systems. Traditional biometric technologies such as fingerprints are susceptible to forgery and less effective in handling external variations, making bioelectric signal-based approaches a promising alternative. By using Analog Discovery 2 to measure the impedance of ten pairs of fingers in the frequency range of 20 kHz to 500 kHz, with a 1 mA sinusoidal current injected into the subject's fingers, real-time data collection can be performed with the precision required for biometric applications. The measurement results show that the impedance value for each finger differs among subjects, making it a useful parameter for biometric authentication. The application of the ratiometric method successfully reduces day-to-day measurement variations, especially at high frequencies above 100 kHz, resulting in more stable and consistent data. This research shows that bioelectrical impedance methods have the potential to improve security compared to traditional methods such as fingerprinting, as they are more difficult to replicate. This approach offers a promising solution for a more secure and highly reproducible biometric authentication system, with potential applications in various security systems and wearable technologies.
Modified Doppler Healthy Pregnancy Monitoring (MODEM-KES) to speed up examinations of pregnant women Cory’ah, Fitra Arsy Nur; Suseno, Mutiara Rachmawati; Faiqah, Syajaratuddur; Megantari, Ayu Dini; Amrinsani, Farid
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.39

Abstract

Monitoring fetal health is a major factor in ensuring a healthy pregnancy and safe delivery. However, in Indonesia, especially in remote areas, limited access to quality health services, lack of sophisticated medical equipment, and difficulty reaching health facilities are serious challenges that contribute to high maternal and infant mortality rates. This study developed MODEM-KES (Modified Doppler Healthy Pregnancy Monitoring) which aims to evaluate the effectiveness of MODEM-KES in supporting health workers, especially midwives in remote areas, in conducting pregnancy monitoring more practically, accurately, and quickly. This tool integrates three important indicators: gestational age estimation, fetal weight estimation, and fetal heart rate, using Doppler sensors and fundus uteri height (FUH) measurements combined with digital methods. The research method involved testing the MODEM-KES prototype against standard tools, such as metline for FUH measurement and Doppler for FHR, with five measurements on each respondent with a gestational age of 26-40 weeks. Results showed that the difference in results between MODEM-KES and standardized tools was relatively small: FUH had a difference of 0-3 cm with an error rate of 0%-10.75%, FHR had a difference of -4/min to 4/min with an error rate of -3.0%-3.1%, and estimated fetal weight had a difference of 0-465 grams with an error rate of 0%-18.8%. Although the accuracy rate varies, MODEM-KES still shows potential as an alternative pregnancy monitoring tool that is practical and easy to use.
Application Of Electrical Impedance Tomography For Detecting Meat (Body Tissue): A Study On Frequency And Amplitude Variations Aisya, Rohadatul; Samatha, Syifa Candiki; Ain, Khusnul; Astuti, Suryani Dyah
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.54

Abstract

Electrical Impedance Tomography (EIT) is an emerging non-invasive imaging technique with significant potential for detecting tissue anomalies; however, its performance is highly sensitive to variations in the frequency and amplitude of the injected electrical signals, which can lead to challenges in accurately differentiating between tissue types and detecting subtle pathological changes. This study aims to optimize EIT performance by systematically investigating the impact of signal frequency and amplitude on image reconstruction quality, thereby enhancing diagnostic accuracy. A portable multi-frequency EIT system was developed using Analog Discovery 2 and MATLAB, featuring a 16-electrode configuration arranged evenly around a tissue phantom, with beef tissue serving as an analog for human tissue due to its comparable conductivity properties. The experimental protocol varied signal amplitudes from 0.4 mA to 1.0 mA and frequencies from 50 kHz to 120 kHz, while two reconstruction algorithms the Gauss-Newton method and the GREIT algorithm were employed to evaluate image quality. Results demonstrated that the Gauss-Newton method achieved superior image clarity, with an approximate 18% improvement in reconstruction accuracy and a 20% reduction in noise at an optimal setting of 100 kHz frequency and 0.8 mA amplitude. Although the GREIT method provided faster reconstruction times, its lower sensitivity to amplitude variations resulted in less detailed anomaly detection. Overall, these findings underscore the critical importance of optimizing electrical parameters in EIT systems to enhance diagnostic capabilities. Future research should focus on integrating machine learning algorithms for real-time image processing and expanding the evaluation to include diverse tissue models to further improve the clinical applicability and robustness of EIT-based diagnostics.
Privacy-Preserving Healthcare Analytics in Indonesia Using Lightweight Blockchain and Federated Learning: Current Landscape and Open Challenges Mardiansyah, Viddi; Bayuaji, Luhur; Herlistiono, Iwa Ovyawan; Violina, Sriyani; Purnama, Adi; Prasetyo, Bagus Alit; Huynh, Phuoc-Hai
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.63

Abstract

Healthcare data are invaluable assets in today’s digital age; however, they are also highly vulnerable to misuse, breaches, and unauthorized access. The global healthcare sector faces a significant dilemma: To leverage exceptionally enormous and heterogeneous datasets, the protection of patient privacy must be ensured while simultaneously improving medical services and public health understanding. In recent years, blockchain technology has emerged as a promising solution to manage healthcare data in a decentralized, transparent, tamperproof, as well as secure way. However, several natural limitations often obstruct many conventional blockchain systems. These limitations include scalability issues, high energy consumption, in addition to increased latency, and they can greatly impede practical adoption in resource-limited settings, particularly in developing countries such as Indonesia. These many limitations considerably spurred developers to create lightweight blockchain frameworks. These frameworks aim to retain all of the core benefits of blockchain, such as its immutability in addition to traceability, and optimize both performance and efficiency. In the event that an individual integrates the proposed system by means of federated learning, which allows training of machine learning models across distributed data sources without data privacy being compromised, the system subsequently offers a compelling solution for healthcare analytics that preserves privacy in its entirety. This paper explores integrated technologies in Indonesian healthcare and highlights their potential and limitations. This study discusses how data can improve services while protecting patient confidentiality despite increasing cyber threats. It also considers regional policies like the Personal Data Protection Law and the BPJS health insurance. Identified are certain open challenges, in addition to particular future research directions, for the purpose of addressing the practical, technical, and regulatory hurdles that must be overcome to realize secure and privacy-aware healthcare analytics in Indonesia.
Acute effects of methadone on neural oscillations: an EEG study of theta, alpha, beta power, and frontal alpha asymmetry in opioid rehabilitation patients Nadiya, Ulfah; Simbolon, Artha Ivonita; Kusumandari, Dwi Esti; Rahmawati, Annida; Amri, M Faizal; Wibowo, Jony Winaryo; Danasasmita, Febrianti Santiardi; Sobana, Siti Aminah; Iskandar, Shelly; Turnip, Arjon
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.64

Abstract

Methadone is a synthetic opioid that commonly employed in opioid substitution therapy (OST) to reduce withdrawal symptoms and suppress cravings in individuals with opioid use disorder. While its pharmacological effects are well-documented, the neurophysiological changes it induces especially during acute administration remain underexplored. This study aims to address that gap by investigating methadone-induced alterations in brain oscillatory activity through electroencephalography (EEG). Specifically, it examines changes in theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequency bands, along with frontal alpha asymmetry (FAA) for F4-F3 and F8-F7, a biomarker associated with emotional and cognitive processing. EEG data were collected from patients enrolled in opioid rehabilitation programs both prior to and one hour following oral methadone intake. The results revealed a significant global decrease in theta power, notably within the frontal, temporal, and occipital cortices. This reduction may reflect changes in executive functioning, emotional regulation, and increased sedation. In contrast, alpha power showed a marked increase, particularly in the central, parietal, and occipital regions, suggesting reduced sensory processing and heightened sedation or attentional disengagement. Meanwhile, beta power was consistently reduced across cortical regions, pointing toward decreased cortical arousal and cognitive alertness. FAA analysis revealed high variability among participants, indicating that methadone's influence on emotional valence and approach-avoidance behavior may differ significantly across individuals. These findings underscore methadone’s sedative and stabilizing effects on neural activity and support its clinical role in managing opioid dependence. Further research into inter-individual differences in EEG responses may inform more personalized and effective OST protocols.
Effectiveness of SMOTE in Enhancing Adult Autism Spectrum Disorder Diagnosis Predictive Performance With Missforest Imputation And Random Forest Musyaffa, Muhammad Hafizh; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Kartini, Dwi; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.66

Abstract

Autism Spectrum Disorder (ASD), originally described by Leo Kanner in 1943, is a complex developmental condition that manifests through social, emotional, and behavioral challenges, often including speech delays and difficulties in interpersonal interactions. Despite significant advancements in diagnostic criteria over the years, accurate diagnosis of ASD in adults remains challenging due to limited access to comprehensive datasets and inherent methodological constraints. The Autism Screening Adult dataset used in this study exemplifies these issues, as it contains missing values and exhibits a marked class imbalance, both of which can adversely affect model performance. To address these challenges, we proposed a framework that integrates Random Forest classification with MissForest imputation and the Synthetic Minority Over-sampling Technique (SMOTE). MissForest effectively imputes missing data by employing an iterative random forest approach that preserves the underlying structure of the data without relying on strict parametric assumptions. Meanwhile, SMOTE generates synthetic samples for the minority class, thereby balancing the dataset and reducing prediction bias. Experimental evaluation through 10-Fold Cross Validation demonstrated that the application of SMOTE significantly enhanced model performance. Notably, the overall accuracy improved from 70.17% to 79.32%, and the AUC-ROC increased from 47.13% to 85.84%, indicating a robust improvement in the model’s ability to distinguish between positive and negative cases. These results underscore the critical importance of addressing data imbalance and missing values in predictive modeling for ASD. The promising outcomes of this study provide a solid foundation for developing more reliable diagnostic tools for adult ASD, and future research may further refine feature selection and incorporate additional data sources to optimize performance even further.
Hybrid Feature Selection and Balancing Data Approach for Improved Software Defect Prediction Febrian, Muhamad Michael; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Abadi, Friska; Herteno, Rudy
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.67

Abstract

Software Defect Prediction (SDP) plays a vital role in identifying defects within software modules. Accurate early detection of software defects can reduce development costs and enhance software reliability. However, SDP remains a significant challenge in the software development lifecycle. This study employs Particle Swarm Optimization (PSO) and addresses several challenges associated with its application, including noisy attributes, high-dimensional data, and imbalanced class distribution. To address these challenges, this study proposed a hybrid filter-based feature selection and class balancing method. The feature selection process incorporates Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Correlation Matrix-Based Feature Selection (CMFS), which have been proven effective in reducing noisy and redundant attributes. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to mitigate class imbalance in the dataset. The K-Nearest Neighbors (KNN) algorithm is employed as the classification model due to its simplicity, non-parametric nature, and suitability for handling the feature subsets produced. Performance evaluation is conducted using the Area Under Curve (AUC) metric with a significance threshold of 0.05 to assess classification capability.  The proposed method achieved an AUC of 0.872, demonstrating its effectiveness in enhancing predictive performance. The proposed method was also superior to other combinations such as PSO SMOTE (0.0043), PSO SMOTE CS (0.0091), PSO SMOTE CFS (0.0111), and PSO SMOTE CFS CMFS (0.0007). The findings of this study show that the proposed method significantly enhances the efficiency and accuracy of PSO in software defect prediction tasks. This hybrid strategy demonstrates strong potential as a robust solution for future research and application in predictive software quality assurance.
Enhancing Imbalanced Data Handling Using MWMOTE and K-Means Clustering Untoro, Meida Cahyo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.69

Abstract

Machine learning and data mining, the quality of a dataset significantly influences model performance. One common issue is data imbalance, where one class in a dataset has significantly fewer samples than another. This imbalance can lead to biased models that favor the majority class, resulting in poor predictive performance for minority class instances. To address this issue, this study employs a resampling approach using the MWMOTE (Majority Weighted Minority Oversampling Technique) method, enhanced with K-Means Clustering. The MWMOTE algorithm generates synthetic samples for the minority class, while K-Means Clustering helps improve the distribution of generated samples by forming well-structured clusters. Experimental results on 10 different datasets demonstrate that the proposed MWMOTE + K-Means approach significantly improves classification performance. Compared to the baseline accuracy of 70%, the proposed method enhances precision by 10%, recall by 40%, and F-measure by 40%. However, the computational cost is slightly increased due to the additional clustering step required for synthetic data generation. Despite the increased computation time, the improvement in classification metrics suggests that integrating K-Means with MWMOTE is a promising technique for handling imbalanced data. Future research could explore optimizing the computational efficiency of this approach and comparing it with other oversampling techniques.

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