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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 287 Documents
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Deep learning Methods for ECG-Based Heart Disease Detection Irsyad, Akhmad; widagdo, Putut Pamilih; Wardhana, Reza
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.498

Abstract

Cardiovascular disease (CVD) continues to be a primary cause of death globally, and early detection plays a critical role in improving patient outcomes. This research introduces the development of a deep learning model designed to automatically categorize heart diseases using Electrocardiogram (ECG) data. The model utilizes a 1D Convolutional Neural Network (CNN) structure and makes use of the MIT-BIH Arrhythmia dataset from Physionet. The dataset was split into training, validation, and testing subsets. Our proposed design incorporates convolutional layers, max-pooling, ReLU activation functions, and dropout layers to prevent overfitting. Comparative assessment against conventional methods such as logistic regression and Support Vector Machines (SVM) shows superior performance, achieving an accuracy of 98.29%, recall of 87.60%, precision of 93.75%, and F1 score of 90.37%. The potential of deep learning to enhance the accuracy and efficiency of diagnosing CVD from ECG data is highlighted in this study, introducing a reliable tool for clinical application.
Comparative Analysis of Hepatitis C virus Genotype 1a (Isolate 1) using Multiple Regression Algorithms and Fingerprinting Techniques Nur Fiat, Daffa; Suratinoyo, Syifabela; Kolang, Indri Claudia; Ticoalu, Injilia Tirza; Purnomo, Nadira Tri Ardianti; Mawara, Reza Michelly Cantika; Sengkey, Daniel; Masengi, Angelina Stevany Regina; Sambul, Alwin Melkie
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.506

Abstract

Approximately 70 million people worldwide have been infected with Hepatitis C virus (HCV), presenting a critical global health challenge. As a member of the Flaviviridae family, HCV can cause severe liver diseases such as cirrhosis, acute hepatitis, and chronic hepatitis. The Hepatitis C virus (HCV) genome encodes a single polyprotein consisting of 3010 amino acids, which when processed contains 10 polypeptides derived from cellular and viral proteases. These include structural proteins such as core protein, E1 and E2 envelope glycoproteins, and nonstructural proteins such as NS1, NS2, NS3, NS4A, NS4B, NS5A, and NS5B. Nonstructural proteins will be released by HCV NS2-3 and NS3-4A proteases, however, structural proteins will be released by host ER signaling peptidases. co-translationally and post-translationally form 10 individual structural proteins: 5'-C-E1-E2-p7-NS2-NS3-NS4A-NS4B-NS5A-NS5B-3'. Despite extensive research, there are significant gaps in predictive and analytical approaches to managing HCV, particularly in understanding the polyprotein structure and its implications for drug discovery. This study addresses these gaps by employing machine learning techniques to analyze HCV polyprotein using various fingerprinting methods and regression algorithms. The data was sourced from the ChEMBL database, and fingerprinting techniques such as PubChem, MACCS, and E-State were utilized. Regression algorithms, including Gradient Boosting Regression (GBR), Random Forest Regression (RFR), AdaBoost Regression (ABR), and Hist Gradient Boosting Regression (HSR), were applied. Model performance was evaluated using R² and Adjusted R² metrics, comparing default models with those enhanced by hyperparameter tuning. Feature importance analysis was conducted to identify key features influencing model performance, aiding in model simplification. The results show that although hyperparameter tuning does not significantly improve the predictive power of a model, it can provide an insight into model optimization. In particular, the default model showed higher R² and Adjusted R² values across different fingerprinting techniques compared to models with hyperparameterized features. Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) consistently performed well, with GBR showing the highest R² values when using PubChem fingerprints. Although there was no significant improvement through hyperparameter tuning, this study was able to find out the features that strongly influenced the model performance by conducting a feature importance analysis. This analysis helped simplify the model and highlighted the potential of machine learning in improving the understanding of HCV polyprotein structure. This research identifies optimal regression models and fingerprinting techniques, providing a strong framework for future drug discovery efforts aimed at improving global health outcomes. The research also shows that it is important to date to advance drug discovery using machine learning.
Dental Caries Segmentation using Deformable Dense Residual Half U-Net for Teledentistry System Iklima, Zendi; Trie Maya Kadarina; Priambodo, Rinto; Riandini, Riandini; Wardhani, Rika Novita; Setiowati, Sulis
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.511

Abstract

Clinical practitioners’ workload and challenges are significantly reduced by classifying, predicting, and localizing lesions or dental caries. In recent research, a high-reliability diagnostic system within deep learning models has been implemented in a clinical teledentistry system. In order to construct an efficient, precise, and lightweight deep learning architecture, it is dynamically structured. In this paper, we present an efficient, accurate, and lightweight deep learning architecture for augmenting spatial locations and improving the transformation modeling abilities of fixed-structure CNNs. Deformable Dense Residual (DDR) enhances the efficacy of the residual convolution block by optimizing its structure, thereby mitigating model redundancy and ameliorating the challenge of vanishing gradients encountered during the training stages. DDR Half U-Net presents notable advancements to the simplified U-Net framework across three pivotal domains: the encoder, decoder, and loss function. Specifically, the encoder integrates deformable convolutions, thereby enhancing the model's capacity to discern features of diverse scales and configurations. In the decoder, a sophisticated arrangement of dense residual connections facilitates the fusion of low-level and high-level features, contributing to comprehensive feature extraction. Moreover, the utilization of a weight-adaptive loss function ensures equitable consideration of both caries and non-caries samples, thereby promoting balanced optimization during training.
Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease Muhammad Mursyidan Amini; Mazdadi, Muhammad Itqan; Muliadi, Muliadi; Faisal, Mohammad Reza; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.561

Abstract

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.
A Specific Marker Approach to Improve Object Recognition in Bullet Launchers with Computer Vision Ahmad, Umar Ali; Tresna, Wildan Panji; Sugiarto, Iyon Titok; Delimayanti, Mera Kartika; Mustofa, Fahmi Charish; Faisal, Mohammad Reza; Septiawan, Reza Rendian
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.460

Abstract

Computer vision’s ability determines the accuracy of object recognition. This study tested the camera's ability to recognize both passive and active markers using LEDs. A specific active marker is analyzed using blinking on the LED. One of the factors to consider when choosing a specific marker is the value of the duty cycle accuracy. The proposed system is confirmed by implementing an integrated control system and the hardware to develop a specific marker. The result shows that the commercial camera can recognize all colors used as the test markers. Here, a specific marker was improved in the bullet launcher system due to tracking, identifying, detecting, marking, locking, and shooting a target precisely. Generally, image processing obtained the comparison of the time to speed the process, the higher the pixel resolution, the longer the time. When the object moves at a certain speed, the camera can detect several marker shapes, such as circles, squares, and triangles. The result shows that a circle marker gives a higher accuracy at every speed level. In the duty cycle variation test, when the duty cycle value is set to 50%, the best accuracy is obtained when the red LED is used, with the accuracy value obtained reaching 96%. In the LED test, it is also found that the effect of light affects the color detection results on the LED. Moreover, using the highest accuracy results from the LEDs at the implementation stage would be very good.
Implementation of Ensemble Machine Learning with Voting Classifier for Reliable Tuberculosis Detection Using Chest X-ray Images with Imbalance Dataset Jauhari, Muhammad I; Wirakusuma, Muhammad P.; Sidqi, Anka; Putra, I Gusti Ngurah R. A.; Wijayanto, Inung; Rizal, Achmad; Hadiyoso, Sugondo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.472

Abstract

Tuberculosis (TB) is an infectious disease caused by bacteria. Tuberculosis is spread through the air and saliva that contain mycobacterium tuberculosis. If not treated immediately, it can spread to other vital organs, such as the heart and liver, and can even lead to death. In this study, we developed a severe tuberculosis detection system using the Tuberculosis (TB) dataset with simple computation. We used 4200 data points (3500 Normal and 700 TB). In other words, this research aimed to create lightweight computation with Machine Learning (Voting Classifier in Ensemble Learning) as the classifier using Imbalance data. Initial experiments used single machine learning with the best-performing models, Support Vector Machine (SVM), and Random Forest as classifiers. With an accuracy of 98.6% and 98%, they were combined using Ensemble Learning without feature extraction; the accuracy, AUC, Recall, Precision, and F1-score using the voting classifier were 99.1%, 99.3%, 99%, 98%, and 98%, respectively.
Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images Azhar, Yufis; Setiono, Fauzan Adrivano; Chandranegara, Didih Rizki
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.487

Abstract

According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of the tongue and teeth. Nevertheless, no study has classified tongue and dental diseases using merged datasets as of yet. This research addresses this gap by focusing on the classification of dental and tongue diseases using transfer learning techniques with CNN architecture models VGG16, VGG19, and ResNet50. The primary aim is to compare these three models to identify the one with the most optimal performance in handling related cases. Based on the results, the best accuracy was achieved with data augmentation and models trained for 75 epochs. The VGG16 model attained 94% accuracy, VGG19 achieved 93% accuracy, and ResNet50 also reached 94% accuracy. These findings suggest that transfer learning with CNN architectures can effectively classify dental and tongue diseases. The implications are significant for developing automated diagnostic tools that can aid in the early detection and treatment of oral health issues globally.
Time Series Classification of Badminton Pose using LSTM with Landmark Tracking Purnama, Bedy; Erfianto, Bayu; Wirawan, Ilo Raditio
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.488

Abstract

Traditional methods of analyzing badminton matches, such as video movement analysis, are time-consuming, prone to errors, and rely heavily on manual annotation. This creates challenges in accurately and efficiently classifying badminton actions and player poses. This paper aims to develop an accurate time series classification method for badminton poses using landmark tracking. The proposed method integrates Long Short-Term Memory (LSTM) networks with landmark tracking to classify badminton poses in a time series, addressing the limitations of traditional video analysis techniques. The dataset consists of 30 respondents performing three distinct activities—lob, smash, and serve—under two conditions: good and bad execution. The approach combines LSTM networks with landmark tracking data, utilizing intra-class variation from a multi-view dataset to enhance pose classification accuracy. The LSTM model achieved high accuracy in classifying badminton poses, successfully detecting serves, lobs, and smashes in real-time with over 90% accuracy. Additionally, the system improved match analysis, achieving 85% accuracy in detection and classification, demonstrating the effectiveness of combining landmark tracking with machine learning for sports analysis. This study underscores the importance of pose estimation in badminton analysis, particularly through landmark tracking, which significantly improves the accuracy of classifying player poses and contributes to the advancement of automated sports analysis.
A Comprehensive Evaluation of Machine Learning Techniques for Forecasting Student Academic Success Abatal, Ahmed; Korchi, Adil; Mzili, Mourad; Mzili, Toufik; Khalouki, Hajar; Billah, Mohammed El Kaim
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.489

Abstract

Improving academic outcomes relies on accurately anticipating student outcomes within a course or program. This predictive capability empowers instructional leaders to optimize the allocation of resources and tailor instruction to meet individual student needs more effectively. In this study, we endeavor to delineate the attributes of machine learning algorithms that excel in forecasting student grades. Leveraging a comprehensive dataset encompassing both personal student information and corresponding grades, we embark on a rigorous evaluation of various regression algorithms. Our analysis encompasses a range of widely used technniques, Incorporating various machine learning algorithms like XGBoost, Linear Regression, K-Nearest Neighbor, Decision Tree, Random Forest, and Deep Neural Network. By conducting thorough comparisons using metrics such as Root Mean Squared Error, determination coefficient, Mean Average Error and Mean Squared Error. Our aim is to pinpoint the algorithm that exhibits superior predictive ability. Notably, our experimental findings unveil the deep neural network as the standout performer among the evaluated algorithms. Having an outstanding coefficient of determination of 99.95% and Minimal error margins, the DNN emerges as a potent tool for accurately forecasting student grades. This discovery not only underscores the efficacy of advanced machine learning methodologies but also underscores the transformative potential they hold in shaping educational practices and optimizing student outcomes.
Deep Learning based classification of ECG signals using RNN and LSTM Mechanism V, Satheeswaran; G.Naga Chandrika; Ankita Mitra; Rini Chowdhury; Prashant Kumar; Glory E
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.496

Abstract

The Electrocardiogram (ECG) stands as a pivotal tool in cardiovascular disease diagnosis, widely embraced within clinical domains for its simplicity and effectiveness. This paper presents a novel method for classifying ECG signals by leveraging deep learning techniques, specifically Long Short-Term Memory (LSTM) networks enhanced with an attention mechanism. ECG signals encapsulate vital insights into cardiac activities and abnormalities, underscoring the importance of precise classification for diagnosing heart conditions. Conventional methods often confront with the intricate variability of ECG signals, prompting the exploration of sophisticated machine learning models. Within this framework, an attention mechanism is seamlessly integrated into the LSTM architecture, dynamically assigning significance to different segments of the input sequence. This adaptive mechanism permits the model to focus on relevant features for classification, thereby bolstering interpretability and performance by highlighting crucial aspects within the ECG signals. Experiments conducted on the MIT/BIH dataset have yielded compelling findings, boasting an impressive overall classification accuracy of 98.9%. Precision stands at 0.993, recall at 0.992, and the F1 score at 0.99, underscoring the robustness of the results. These findings underscore the potential of the proposed methodology in significantly enhancing ECG signal analysis, thereby facilitating more accurate diagnosis and treatment decisions in the realm of cardiac healthcare.

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