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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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Optimizing Categorical Boosting Model with Optuna for Anti-Tuberculosis Drugs Classification Yosua Satria Bara Harmoni; Kartika Maulida Hindrayani; Dwi Arman Prasetya
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.ijeeemi.v7i2.92

Abstract

Tuberculosis is one of the leading causes of death globally, with death rate reaching 1.30 million by 2022, an increase of 3.2% compared to the previous year. Indonesia is one of the countries with the highest number of tuberculosis cases in the world. The Directly Observed Treatment Short-course (DOTS) plays a role in improving the effectiveness of tuberculosis therapy by ensuring the availability of appropriate anti-tuberculosis drugs. However, errors in drug selection can lead to therapy failure, relapse, and Multi-Drug Resistant (MDR) cases. To overcome this, classification models based on patient medical record data can be used to improve the accuracy of drug selection. This research focuses on developing classification model to determine the type of drug using Categorical Boosting algorithm optimized with Optuna using Tree-structured Parzen Estimator. The data consisted of numerical variables, such as age, treatment duration, and categorical variables, such as history of diabetes mellitus, HIV status, drug combination. The CatBoost algorithm was chosen due to its ability to handle categorical data. Hyperparameter optimization was performed to obtain the best parameters. The preprocessing stage involved memory reduction, feature normalization, and encoding on 620 data samples, which were then divided into 90% training and 10% test data. Experimental results show CatBoost model produces an initial accuracy of 90%. After applying parameter optimization techniques using Optuna, the accuracy increased to 96%, showing 6% improvement. The model is able to accurately classify drugs combination, which can support the selection of more effective therapies for tuberculosis patients. Thus, the use of SMOTE to address class imbalance combined with Optuna for hyperparameter optimization was shown to improve the accuracy of CatBoost-based classification models. This finding confirms the effectiveness of SMOTE and Optuna methods in improving the accuracy of prediction models for drug type classification, contributing the improvement of tuberculosis treatment strategies.
Application of Adaboost Algorithm with SMOTE and Optuna Techniques in Sleep Disorder Classification Anshory, Muhammad Naufal; Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Saputro, Setyo Wahyu
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.v7i2.99

Abstract

Data imbalance is a serious challenge in developing machine learning models for sleep disorder classification. When models are trained on an uneven distribution of classes, classification performance for minority classes such as insomnia and sleep apnea is often low. As a result, the overall accuracy may seem elevated, yet the sensitivity to important cases to be weak. Therefore, this research aims to design and develop a robust sleep disorder classification model with the AdaBoost algorithm, with improved performance through the integration of two main approaches, namely data balancing technique utilizing SMOTE and hyperparameter optimization using Optuna. This research contributes by showing that the combination of the two approaches can significantly improve model performance, not only in terms of global accuracy, but also accuracy on previously overlooked minority classes. The dataset utilized is the Sleep Health and Lifestyle Dataset which consists of 374 synthesized data and is divided into three categories: insomnia, sleep apnea, and none. This method stages include data preprocessing, data division using train-test split (80:20), application of SMOTE to balance the class distribution, hyperparameter tuning using Optuna, and model training with the AdaBoost algorithm. Evaluation was performed using classification metrics: accuracy, precision, recall, and F1-score. Results showed that mix of SMOTE and Optuna yielded the best results, accuracy 90.6%, F1-score 0.83871 for insomnia, and 0.81250 for sleep apnea. This performance was consistently superior to scenarios with no SMOTE or no tuning. This confirms the importance of using combination strategies to obtain fair and accurate classification on medical data. Future research is recommended to use real datasets as well as test the capabilities of this research on other models such as XGBoost or LightGBM.
EEG-Based Emotion Classification in Response to Humorous, Sad, and Fearful Video Stimuli Using LSTM Networks: A Comparative Study with Classical Machine Learning Models Muhamad Agung Suhendra; Tedi Sumardi; Iqbal Robiyana; Nurizati, Nurizati
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.v7i2.100

Abstract

Emotion recognition based on EEG signals is a critical area within affective computing, with applications in mental health monitoring, human-computer interaction, and neuroadaptive systems. However, accurately classifying emotional states from inherently non-stationary and noisy EEG data remains a major challenge. This study explores the classification of three discrete emotions, Humorous, Sad, and Fearful, elicited through video stimuli, using EEG recordings from six participants acquired via a 19-channel Mitsar amplifier at a 500 Hz sampling rate. Preprocessing steps included bandpass filtering (1–40 Hz), epoch segmentation, and multi-domain feature extraction encompassing statistical measures, spectral features, differential entropy, Hjorth parameters, and hemispheric asymmetry indicators. Data augmentation was applied to balance class distributions, particularly for the underrepresented fear category. The resulting features were normalized and structured to support temporal deep learning and classical machine learning models. The classification performance of Long Short-Term Memory (LSTM) networks was evaluated alongside Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) classifiers. While LSTM demonstrated competency in capturing temporal dependencies, especially in fear recognition, SVM achieved the highest overall accuracy, 94.12%, outperforming LSTM at 85.16%, RF at 90.00%, and k-NN at 78.01%. These results suggest that when robust and discriminative features are employed, traditional models like SVM can surpass deep learning methods, particularly in small-scale EEG datasets with limited temporal complexity. This study underscores the importance of aligning model architecture with feature representation and contributes a comparative evaluation framework for EEG-based emotion recognition systems.
Hybrid features to classify lung tumor using machine learning Rahmawan, Rizki Dwi; Salamah, Umi; Yudha, Ery Permana
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.v7i2.101

Abstract

A lung tumor is an abnormal mass of cells inside a body. As a benign tumor is unproblematic, but a malignant tumor is cancerous because it can travel across the body and interfere with its surrounding tissue. Detecting these cancerous cells in the lung is important because delayed detection may hamper effective treatment options, leading to a lower survival rate. However, classifying tumor malignancy is highly dependent on the knowledge and experience of the radiologist. This study combines texture-based features extracted from lung Computed Tomography Scan (CT Scan) images such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRLM), Gray Level Size-zone Matrix (GLSZM), and Haralick Features aims to create a lung tumor classification system. This research contributes by creating an efficient and reliable system through Relief-F feature selection that uses features with the highest weight in rank that are able to differentiate classes of tumor malignancy and help medical professionals diagnose tumors more early in the treatment.  As a comparison, several conventional machine learning classifiers, including SVM RBF, KNN, RF, DT, and XGBoost, were utilized to evaluate classifier performance. The result showed that the accuracy of the proposed hybrid features with a random forest classifier was the most performing approach with an evaluation score of accuracy of 99.55%, precision of 99.55%, recall of 99.55%, and F1-Score of 99.54%. Furthermore, accuracy among other classifiers was also higher than 90%. Proofing the selected features retain essential class information, demonstrating the study’s applicability in developing automated lung tumor classification systems from CT scans.
Performance Comparison of Variational Mode Decomposition and Butterworth in Processing EEG Signals of Autism Patients Wardana, Surya; Melinda, Melinda; Ramdhana, Rizka; Yunidar, Yunidar; Away, Yuwaldi; Basir, Nurlida
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.105

Abstract

Electroencephalography (EEG) is a non-invasive technique for monitoring and recording the brain's electrical activity with electrodes applied to the scalp. The method is important in neurological studies, like that of Autism Spectrum Disorder (ASD), because it measures patterns of brain waves that can identify developmental abnormalities. However, EEG signals are often contaminated by multiple noise sources, including eye movements, muscle activity, and extraneous interference. This interference can significantly reduce the quality and intelligibility of signals. Therefore, preprocessing is required to enhance the reliability and precision of the data obtained. In this study, a Butterworth Band-Pass Filter (BPF) was used during preprocessing to filter out undesirable frequency components and to mitigate noise. After filtering, EEG signals were handled using the Variational Mode Decomposition (VMD) technique. VMD is an adaptive method for decomposing multidimensional signals into intrinsic mode functions while preserving critical details of the original data. For performance comparison, four quantitative metrics were used: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). Results showed that VMD performed better than BPF alone. As an example, for Subject 1, VMD achieved an MAE of 0.26 and MSE of 0.42, which was far superior to the MAE of 13.72 and MSE of 674.96 of BPF. Subject 3 had the least RMSE (0.40) when using VMD, whereas BPF scored 25.90. VMD also reported a highest SNR of 28.56, compared to BPF's 2.43. Overall, integrating VMD with BPF significantly improves EEG signal quality and enables more accurate analysis, particularly in ASD-related studies.
Convolutional Kolmogorov-Arnold Network for Pneumonia Detection in Medical Image Analysis Riechie, Riechie; Jessica, Vira; Kurniawan, Matthew; Samosir, Feliks Victor Parningotan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.106

Abstract

Pneumonia is a serious respiratory infection that poses a significant global health burden, particularly in regions with limited access to medical personnel and diagnostic resources. Chest X-ray imaging remains the most common method for pneumonia diagnosis, however, manual interpretation is prone to error and often requires experienced radiologists. To address this challenge, automated diagnostic systems based on deep learning have gained increasing attention. This study aims to evaluate the effectiveness of the Convolutional Kolmogorov-Arnold Network (CKAN) in detecting pneumonia from chest X-ray images and compare its performance against a baseline Convolutional Neural Network (CNN) model. The study involved three variations of CKAN architecture that combined convolutional layers with Kolmogorov-Arnold-based layers. Both CKAN and CNN models were trained on balanced and imbalanced datasets using data augmentation techniques to improve model robustness. Additional experiments were conducted with and without the application of early stopping mechanisms. Performance evaluation was conducted using five metrics: accuracy, precision, recall, specificity, and balanced accuracy. Loss history and confusion matrices were also analyzed to assess learning stability and classification behavior. The best-performing CKAN model achieved an accuracy of 83.49%, precision of 79.96%, recall of 98.21%, specificity of 78.59%, and balanced accuracy of 78.59%. In comparison, the best-performing CNN model reached 81%, 77.98%, 97.18%, 75.73%, and 75.73%, respectively. These results demonstrate CKAN’s superior generalization capability and its effectiveness in handling class imbalance. In conclusion, CKAN shows promising potential for improving pneumonia detection from chest X-rays using a more compact and interpretable model structure. Future studies can explore hyperparameter optimization and extend the method to other medical imaging tasks. This work contributes to the development of more accurate and accessible automated diagnostic systems.
Autism EEG Signal Pre-Processing: Performance Evaluation of MS-ICA and Butterworth Filter Mirza Rahmat, Muhammad; Nurdin, Yudha; Melinda, Melinda; Away, Yuwaldi; Irhamsyah, Muhammad; Wong, W. K
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.107

Abstract

Autism Spectrum Disorder (ASD) is a neurological condition characterized by challenges in communication and social interaction, accompanied by the development of repetitive behavioral patterns. Electroencephalography (EEG) is primarily used to assess brain function in children with Autism Spectrum Disorder (ASD), mainly due to its non-invasive nature and superior temporal resolution compared to other neuroimaging methods. However, EEG signals are often contaminated by biological artifacts, such as eye movements and muscle contractions, which can significantly distort analysis outcomes. Pre-processing is therefore required to increase the accuracy of the EEG signal before additional analysis. The goal of this study was to compare and evaluate the performance of two pre-processing techniques, the Butterworth Band-Pass Filter and Multiscale Independent Component Analysis (MS-ICA), using four different performance metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). The Butterworth method has an MAE of 227.57, which is acceptable. However, it produced an MSE of 160,653.22, an RMSE of 394.49, and a maximum SNR of only 1.33 dB. MS-ICA performs far better with a best MAE of only 0.44, an MSE of 3.33, an RMSE of 1.76, and an SNR of 30.88 dB. Paired t-test (p < 0.05) was employed to determine statistical significance,  while Cohen's d was used to assess the practical significance of the results. The effect sizes of MAE (d = 1.60), MSE (d = 1.02), RMSE (d = 1.54), and SNR (d = -9.50) were all calculated as large. These findings demonstrate that MS-ICA offers both statistical advantages and strong practical usefulness for noise removal while preserving the structural integrity of the original EEG signals. Therefore, MS-ICA proves to be the best approach for pre-processing EEG signals to be used for analysis in children with ASD
Performance Evaluation of EfficientNetB3-Based Deep Learning Model for the Classification of Acute Lymphoblastic Leukemia and Normal Blood Cells Muchallil, Sayed; Fitria, Maya; Arrahman, Ridha; Saddami, Khairun
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.113

Abstract

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer that predominantly affects children and requires early and accurate diagnosis to improve patient survival rates. Traditional diagnostic methods rely heavily on manual examination of blood smear images by pathologists, which is not only time-consuming but also susceptible to human error and variability. To address this limitation, this study proposed an automated detection model based on deep learning, specifically employing the EfficientNetB3 convolutional neural network architecture. A publicly available dataset containing microscopic images of ALL and normal blood cells was used for training and evaluation. The images were preprocessed using normalization and augmentation techniques and resized to 300×300 pixels to align with the EfficientNetB3 input requirements. The model was trained using the Adam optimizer and monitored with EarlyStopping to prevent overfitting. Experimental results showed that the proposed model achieved an accuracy of 92.23%, precision of 92.75%, and recall of 95.57%, significantly outperforming conventional approaches such as Canberra distance, K-Nearest Neighbor, and ensemble CNN methods. In addition to the classification model, a web-based ALL detection system was developed to make the solution more accessible and user-friendly. The frontend was built using ReactJS, while the backend API, built with Flask, handles image input, model inference, and output delivery. The interface allows users to upload cell images, input patient names, and receive instant classification results along with confidence scores. This integrated system demonstrates a practical application of AI in medical diagnostics and holds potential for use in real-world, resource-limited clinical settings.
An Empirical Study of Cross-Project and Within-Project Performance in Software Defect Prediction Models Using Tree-Based and Boosting Classifiers Raidra Zeniananto; Herteno, Rudy; Radityo Adi Nugroho; Andi Farmadi; Setyo Wahyu Saputro
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.95

Abstract

Software Defect Prediction (SDP) is a vital process in modern software engineering aimed at identifying faulty components in the early stages of development. In this study, we conducted a comprehensive evaluation of two widely employed SDP approaches, Within-Project Software Defect Prediction (WP-SDP) and Cross-Project Software Defect Prediction (CP-SDP), using identical preprocessing steps to ensure an objective comparison. We utilized the NASA MDP dataset, where each project was split into 70% training and 30% testing data, and applied three distinct resampling strategies—no sampling, oversampling, and undersampling—to address the challenge of class imbalance. Five classification algorithms were examined, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), and LightGBM (LGBM). Performance was measured primarily using Accuracy and Area Under the Curve (AUC) metrics, resulting in 360 experimental outcomes. Our findings revealed that WP-SDP, combined with oversampling and Random Forest, demonstrated superior predictive capability on most projects, achieving an Accuracy of 89.92% and an AUC of 0.931 on PC4. Nonetheless, CP-SDP excelled in certain small-scale projects (e.g., MW1), underscoring its potential when local historical data is scarce but inter-project characteristics remain sufficiently similar. This study’s results underscore the importance of selecting a prediction scheme tailored to specific project attributes, class imbalance levels, and available historical data. By establishing a standardized methodological framework, our work contributes to a clearer understanding of the strengths and limitations of WP-SDP and CP-SDP, paving the way for more effective defect detection strategies and improved software quality.
Implementation of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) Method to Address Class Imbalance in Alzheimer’s Disease Magnetic Resonance Imaging (MRI) Datasets Alamudin, Muhammad Faiq; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Saragih, Triando Hamonangan; Muliadi, Muliadi; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.109

Abstract

Class imbalance in medical imaging datasets often leads to biased machine learning models, particularly in Alzheimer’s disease (AD) diagnosis using MRI. This study proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to mitigate class imbalance in AD MRI datasets. Realistic MRI images were synthesized for underrepresented AD stages, and the quality of the generated data was quantitatively validatedusing the Fréchet Inception Distance (FID), with the lowest FID score recorded at 31.84, indicating a high degree of realism and diversity. The synthetic images were used to augment a dataset of 6,400 T1-weighted scans for training four Convolutional Neural Network (CNN) architectures: ResNet-50, AlexNet, VGG-16, and VGG-19. Results demonstrated statistically significant improvements in balanced accuracy across all models (p < 0.01 for all comparisons). The AlexNet + WGAN-GP combination achieved the highest accuracy of 98.54%, representing a mean improvement of 4.76% (95% CI: 2.45% to 6.98%) over its baseline. Significant gains were also observed for ResNet-50, VGG-16, and VGG-19. These enhancements were consistent across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These findings confirm that WGAN-GP is a highly effective and statistically validated strategy for boosting the diagnostic accuracy of CNN models in Alzheimer's disease classification

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