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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 12 Documents
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Search results for , issue "Vol 6 No 2 (2024): April" : 12 Documents clear
Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models Ghavidel, Arman; Pazos, Pilar; Del Aguila Suarez, Rolando; Atashi, Alireza
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.359

Abstract

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model's superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models.
An Approach to ECG-based Gender Recognition Using Random Forest Algorithm Arif, Nuuruddin Hamid; Faisal, Mohammad Reza; Farmadi, Andi; Nugrahadi, Dodon; Abadi, Friska; Ahmad, Umar Ali
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.363

Abstract

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.
Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction Suryadi, Mulia Kevin; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.375

Abstract

Software defect prediction is necessary for desktop and mobile applications. Random Forest defect prediction performance can be significantly increased with the parameter optimization process compared to the default parameter. However, the parameter tuning step is commonly neglected. Random Forest has numerous parameters that can be tuned, as a result manually adjusting parameters would diminish the efficiency of Random Forest, yield suboptimal results and it will take a lot of time. This research aims to improve the performance of Random Forest classification by using SMOTE to balance the data, Genetic Algorithm as selection feature, and using hyperparameter tuning to optimize the performance. Apart from that, it is also to find out which hyperparameter tuning method produces the best improvement on the Random Forest classification method. The dataset used in this study is NASA MDP which included 13 datasets. The method used contains SMOTE to handle imbalance data, Genetic Algorithm feature selection, Random Forest classification, and hyperparameter tuning methods including Grid Search, Random Search, Optuna, Bayesian (with Hyperopt), Hyperband, TPE and Nevergrad. The results of this research were carried out by evaluating performance using accuracy and AUC values. In terms of accuracy improvement, the three best methods are Nevergrad, TPE, and Hyperband. In terms of AUC improvement, the three best methods are Hyperband, Optuna, and Random Search. Nevergrad on average improves accuracy by about 3.9% and Hyperband on average improves AUC by about 3.51%. This study indicates that the use of hyperparameter tuning improves Random Forest performance and among all the hyperparameter tuning methods used, Hyperband has the best hyperparameter tuning performance with the highest average increase in both accuracy and AUC. The implication of this research is to increase the use of hyperparameter tuning in software defect prediction and improve software defect prediction performance.
Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning Al Habesyah, Noor Zalekha; Herteno, Rudy; Indriani, Fatma; Budiman, Irwan; Kartini, Dwi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.381

Abstract

TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.
Comparison of CatBoost and Random Forest Methods for Lung Cancer Classification using Hyperparameter Tuning Bayesian Optimization-based Zamzam, Yra Fatria; Saragih, Triando Hamonangan; Herteno, Rudy; Muliadi; Nugrahadi, Dodon Turianto; Huynh, Phuoc-Hai
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.382

Abstract

Lung Cancer is a disease that has a high mortality rate and is often difficult to detect until it reaches a very severe stage. Data indicates that lung cancer cases are typically diagnosed late, posing significant challenges to effective treatment. Early detection efforts offer potential for better recovery chances. Therefore, this research aims to develop methods for the identification and classification of lung cancer in the hope of providing further knowledge on effective ways to detect this condition at an early stage. One approach under scrutiny involves employing machine learning classification techniques, anticipated to serve as a pivotal tool in early disease detection and enhancing patient survival rates. This study involves five stages: data collection, data preprocessing, data partitioning for training and testing using 10-fold cross validation, model training, and analysis of evaluation results. In this research, four experiments consist of applying two classification methods, CatBoost and Random Forest, each tested using default hyperparameter and hyperparameter tuning using Bayesian Optimization. It was found that the Random Forest model using hyperparameter tuning Bayesian Optimization outperformed the other models with accuracy (0.97106), precision (0.97339), recall (0.97185), f-measure (0.97011), and AUC (0.99974) for lung cancer data. These findings highlight Bayesian Optimization for hyperparameter tuning in classification models can improve clinical prediction of lung cancer from patient medical records. The integration of Bayesian Optimization in hyperparameter tuning represents a significant step forward in refining the accuracy and effectiveness of classification models, thus contributing to the ongoing enhancement of medical diagnostics and healthcare strategies.
Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach Nawaz, Muhammad Qasim; Jiang, Wei; Usman, Muhammad; Aimal Khan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.385

Abstract

This study introduces an enhanced Maximum Power Point Tracking (MPPT) control strategy for Direct Drive Permanent Magnet Synchronous Generators (D-PMSG) utilizing a Fuzzy Gradient Step Approach. By comparing with traditional MPPT methodologies, this approach demonstrates significant improvements in tracking accuracy, efficiency, and response time to fluctuating environmental conditions. The fuzzy logic control method adapts dynamically, optimizing power output under variable wind speeds. The comparative analysis reveals that our method not only surpasses conventional techniques in performance but also offers a cost-effective solution with less complexity. Implications of these advancements suggest potential applications in optimizing wind energy systems, enhancing the viability of renewable energy sources. By examining the relationship between Boost duty cycle changes and wind turbine output characteristics, a fuzzy gradient step hill-climbing search method is proposed to calculate the wind turbine output speed in order to improve the maximum power point tracking control performance of the direct-drive permanent magnet wind power generation system. The fuzzy controller employs the duty cycle of the Boost converter as its output quantity and its input quantity to accomplish the maximum power point tracking control of the wind turbine. A model was developed and verified through simulation for use in system modeling. The results show that the fuzzy gradient step hill-climbing search approach is more effective at regulating the maximum power point tracking control of the direct-drive permanent magnet wind power producing system than the traditional variable step-size hill-climbing search algorithm. This research paves the way for future exploration in smart grid integration and scalability of fuzzy logic-based MPPT controllers, marking a pivotal step towards sustainable energy solutions.
Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm Angga Maulana Akbar; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.388

Abstract

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately
QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images Chandrika, G.Naga; Karpagam, J.; Richard, Titus; Shadrach, Finney Daniel; Triwiyanto, T
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.391

Abstract

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.
Insights into Nerve Signal Propagation: The Effect of Extracellular Space in Governing Neuronal Signal for healthy and injured Nerve Fiber using Modified Cable Model Das, Biswajit; Bujar Baruah, Satyabrat Malla; Singh, Sneha; Roy, Soumik
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.394

Abstract

Nerve injuries are complex medical conditions that may arise from a variety of traumatic events or diseases, altering the intricate structure of neural pathways. During neuronal injury, the potassium and sodium ion concentration that controls signaling endures significant changes such as the ion channels getting blocked or an increase in the intracellular ionic concentration. The Extracellular Space which surrounds a nerve fiber has a significant impact on the neuronal signal and variation in its size can alter neuronal signal transmission. Hence, to fully understand neuronal signal transmission, it is essential to explore the effect that the Extracellular Space exerts on the neuronal signal. The aim of this study is to develop a mathematical model which yields a simplistic yet robust mathematical expression of the nerve membrane potential, incorporating the Extracellular Space dependent parameters for having a holistic approach towards understanding neuronal signal transmission in healthy and injured nerve fiber. The conventional cable model focuses solely on the intrinsic properties of the nerve fiber, but the current work expands this model by incorporating the Extracellular Space dependent parameters into the final membrane potential expression. The results obtained from this study shows that certain combination of the Extracellular Space and fiber diameter could bring about hyperexcitation whereas in some cases it may lead to hypoexcitation to the neuronal signal as it propagates along the nerve fiber. Moreover, prolonged refractory period and delayed refractory period are also observed in certain combination of the Extracellular Space and fiber diameter. The proposed framework manages to show trends associated with certain medical conditions and may also be useful to further understand, and early diagnosis of various neurological conditions under the effect of an Extracellular Space of varied sizes.

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