cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Triwiyanto
Contact Email
triwiyanto123@gmail.com
Phone
+628155126883
Journal Mail Official
editorial.jeeemi@gmail.com
Editorial Address
Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya Jl. Pucang Jajar Timur No. 10, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
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 20 Documents
 1   2 
Search results for , issue "Vol 6 No 4 (2024): October" : 20 Documents clear
Early Detection Of Canine Babesia From Red Blood Cell Images Using Deep Ensemble Learning Baruah, Dilip Kumar; Boruah, Kuntala
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.v6i4.484

Abstract

Artificial intelligence-assisted medical diagnosis is enhancing accuracy with the contribution of several state of the art technologies such as Deep Learning (DL), Machine Learning (ML) and Image Processing (IP). From the detection of diseases to the selection of proper treatment plans, AI-powered assistance is effectively employed by healthcare professionals. Despite these advancements, the application of AI in animal healthcare is lagging behind, presenting a significant scope for AI adoption in veterinary medical diagnostics. This study addresses this gap by focusing on the automated diagnosis of canine Babesia infection, a parasitic disease that affects red blood cells (RBC). Our research contributed by developing a labeled dataset of microscopic images of red blood cells of infected and uninfected cases. During this work, four AI models are developed for automated classification: a custom Convolutional Neural Network (CNN), two pre-trained models (VGG16 ,DenseNet121) and a hybrid model (DenseNet121 + Support Vector Machine (SVM)). The performance of these models was 96.88%, 94%, 96.37% and 95.50% respectively. To further enhance the accuracy, a weighted average ensemble technique was employed. The ensemble model achieved an improved accuracy of 97.75%, demonstrating its potential. The enhanced performance of the ensemble model highlights the effectiveness of our method, significantly outperforming traditional methods and providing veterinarians with an efficient early diagnosis tool. This study is one of the few to address disease detection from microscopic images in animals using the potential of Artificial Intelligence.
A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model Patil, Rohini; Anant Patil; Surekha Janrao; Sandip Bankar; Kamal Shah
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.v6i4.497

Abstract

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.
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.
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.
Cybersentinel: The Cyberbullying Detection Application Based on Machine Learning and VADER Lexicon with GridSearchCV Optimization Ernawati, Siti; Frieyadie, Frieyadie; Yulia, Eka Rini
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.v6i4.580

Abstract

Cyberbullying is becoming an increasingly troubling issue in today's digital age, with serious impacts on the well-being of individuals and society as a whole. With the number of social media users continuously rising, there is an urgent need to develop effective solutions for detecting cyberbullying. This urgency negatively affects the well-being of individuals, especially children and adolescents. The Big Data era also brings many new challenges, including the ability of organizations to manage, process, and extract value from available data to generate useful information. The aim of this research is to develop Cybersentinel, a cyberbullying detection application that combines Machine Learning and VADER Lexicon approaches to improve classification accuracy. It involves comparing several Machine Learning algorithms optimized using the GridSearchCV technique to find the best combination of parameters. The dataset used consists of social media comments labeled as bullying and non-bullying. The successfully developed model uses the Support Vector Machnine algorithm, achieving a best accuracy of 98.83%. The system is developed using Python with the Streamlit framework. This application development follows the Design Science Research (DSR) approach, which integrates principles, practices, and procedures to facilitate problem-solving and support the design and creation of applications. Testing is conducted using blackbox testing. The results show that parameter optimization using GridSearchCV can significantly enhance model performance, and applying the DSR method allows for the development of Cybersentinel tailored to specific needs. Thus, Cybersentinel provides an effective solution for detecting cyberbullying and contributes to improving the safety of social media users.

Page 2 of 2 | Total Record : 20

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 8 No 2 (2026): April Vol 8 No 1 (2026): January Vol 7 No 4 (2025): October Vol 7 No 3 (2025): July Vol 7 No 2 (2025): April Vol 7 No 1 (2025): January Vol 6 No 4 (2024): October Vol 6 No 3 (2024): July Vol 6 No 2 (2024): April Vol 6 No 1 (2024): January Vol 5 No 4 (2023): October Vol 5 No 3 (2023): July Vol 5 No 2 (2023): April Vol 5 No 1 (2023): January Vol 4 No 4 (2022): October Vol 4 No 3 (2022): July Vol 4 No 2 (2022): April Vol 4 No 1 (2022): January Vol 3 No 3 (2021): October Vol 3 No 2 (2021): July Vol 3 No 1 (2021): January Vol 2 No 3 (2020): October Vol 2 No 2 (2020): July Vol 2 No 1 (2020): January Vol 1 No 2 (2019): October Vol 1 No 1 (2019): July More Issue