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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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Development of a Surveilens System as a Means for Collection and Reporting of Infectious Diseases in Puskodono Health Center, Sragen Sari, Kiki Puspita; Adi, Kusworo; Agushybana, Farid
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.317

Abstract

Surveillance is an important activity in the context of disease data collection. The results of these surveillance activities can also be used as a consideration for making decisions related to disease management, especially infectious diseases. The aim of the research was to develop an information system for surveillance of infectious diseases for data collection and reporting to support management decision making at the Sukodono Health Center, Sragen.This research method is qualitative and quantitative. Prototype model research development. The research population is all parties involved in the development of an Infectious Disease Management Information System at the Sukodono Health Center, Sragen. The subjects of this study were the Head of the Community Health Center, the Head of the Infectious Diseases Program and the four Communicable Diseases Program Holders. the research object is the Communicable Disease Health Center Management Information System. This research method is qualitative and quantitative. Prototype model research development. The subjects of this study were one head of the health center, one head of the infectious disease program and four holders of the infectious disease program. the research object is the Communicable Disease Health Center Management Information System. Data analysis in this study was carried out by means of content analysis for qualitative and descriptive analysis methods for quantitative. The acquisition of the evaluation results from the total average percentage is Accurate at (62.19%), Complete at (83.89%), timeliness (84.44%), and Relevant at (95.20%). Meanwhile, in terms of data accuracy (accuracy) and completeness of content or data availability (content), information system users are very satisfied, as indicated by the results of the total average percentage of both 80.36 and 99.36% indicating that the user is very confident in the completeness and correctness of the examination data that has been inputted into the information system by paramedics. The results of the evaluation of the information system as a whole are 81.43%, meaning that the Information System for Surveillance of Communicable Diseases can be received and used by users of the information system very well
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media Yunida, Rahmi; Faisal, Mohammad Reza; Muliadi; Indriani, Fatma; Abadi, Friska; Budiman, Irwan; Prastya, Septyan Eka
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.319

Abstract

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.
Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection Ansyari, Muhammad Ridho; Mazdadi, Muhammad Itqan; Indriani, Fatma; Kartini, Dwi; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.322

Abstract

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.
Empowering Rural Healthcare: MobileNet-Driven Deep Learning for Early Diabetic Retinopathy Detection in Nepal Bhatta, Saurav
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.326

Abstract

Diabetic retinopathy (DR) is a pervasive public health challenge, particularly in underserved rural regions like Nepal. Timely detection and management of DR are imperative to prevent vision impairment and blindness among individuals with diabetes. However, the scarcity of advanced medical facilities and trained ophthalmologists in remote areas hampers early diagnosis. This study addresses this critical issue by harnessing deep learning technology, specifically MobileNet, to develop an accessible and cost-effective solution for the early detection of diabetic retinopathy in Nepal's rural communities. The research aims to design and implement a robust DR detection system capable of operating on resource-constrained mobile devices. MobileNet, renowned for its efficiency and suitability for mobile applications, serves as the cornerstone of the solution. Through fine-tuning MobileNet on a comprehensive dataset comprising diverse DR manifestations, the model learns to accurately classify the presence of retinopathy. Recognizing the challenge of limited internet connectivity in rural Nepal, the project explores on-device inference techniques. This enables the model to run directly on mobile devices, minimizing the dependence on continuous internet access and making the diagnostic tool available even in remote regions. The anticipated impact of this project is twofold: firstly, it empowers healthcare workers in rural Nepal to provide early and precise DR screenings, facilitating timely intervention and reducing avoidable blindness. Secondly, it demonstrates the feasibility of deploying deep learning and mobile technology to address healthcare challenges in resource-limited settings beyond diabetic retinopathy.By harnessing MobileNet-based deep learning, this solution has the potential to significantly enhance healthcare accessibility and contribute to the overall well-being of the diabetic population in underserved areas.
Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine Muhamad Fawwaz Akbar; Muhammad Itqan Mazdadi; Muliadi; Triando Hamonangan Saragih; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.328

Abstract

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.
A Novel Tourniquet with an Alarm System, Replaceable Components, and The Ability to Adjust Pressure and Detect Body Temperature for Medical Applications Bameri, Mohammad Mahdi; Abolhassani, Moussa; Bameri, Mohammad Hasan; Shafaghi, Shadi; Ghorbani, Fariba; Shafaghi, Masoud
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.329

Abstract

A tourniquet is a practical device in the medical field that is employed to collect blood and prevent bleeding in medical centers. The need for a durable tourniquet with leveled pressure adjustment capabilities, which indicates the time, can be used for both blood collection and control of intense bleeding, and has replaceable components in case of damage is strongly felt. Thus, developing a tourniquet with the aforementioned features is highly beneficial. The designed tourniquet includes four main parts (end hook, strap, main clip, and pin), and the other three parts are mounted on the strap. This tourniquet has a unique strap design and a sturdy lock and fastener, a body temperature detection sensor, a pressure adjustment section at four levels, a timer with an alarm, buttons (pins) to open the strap, a linen pad, and a special end hook (with an end clamp). The proposed time warning tourniquet with replaceable components has the ability to adjust the pressure, detect the temperature, and create local pressure, and therefore, can boost the performance of the medical staff during blood sampling and bleeding control procedures.
How Deep Learning and Neural Networks can Improve Prosthetics and Exoskeletons: A Review of State-of-the-Art Methods and Challenges Triwiyanto, Triwiyanto; Caesarendra, Wahyu; Ahmed, Abdussalam Ali; V.H, Abdullayev
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.333

Abstract

Deep learning and neural networks are powerful computational methods that have been widely applied in various fields, such as healthcare and robotics. In this paper, we review some of the recent research studies that use deep learning and neural networks in healthcare and robotics, particularly focusing on their application in prosthetics and exoskeletons. The main source of data for this review is Scopus, which is a large and multidisciplinary database of peer-reviewed literature. The search criteria for this review are exoskeleton AND prosthetic AND deep AND learning. The search is limited to documents published from 2014 to 2023, as this period covers the recent developments and trends in the field4. The search results in 488 documents that match the criteria. We selected 20 papers that represent the state-of-the-art methods and applications of deep learning and neural networks in prosthetics and exoskeletons. We categorized these papers by various attributes, such as document type, subject area, sensor type, respondent, condition, etc. The main finding of this paper was that deep learning techniques and neural networks have diverse and transformative potential in healthcare and robotics, especially in the development and improvement of prosthetics and exoskeletons. The paper highlighted how these advanced computational methods can be harnessed to interpret complex biological signals, improve device functionality, enhance user safety, and ultimately improve quality of life for individuals using these devices. The paper also identified some possible future directions for this topic, such as exploring the impact of deep learning techniques and neural networks on the performance, usability, and user satisfaction of prosthetics and exoskeletons. This paper provided a valuable insight into the current state-of-the-art and future prospects of deep learning techniques and neural networks in healthcare and robotics
Implementation of Monarch Butterfly Optimization for Feature Selection in Coronary Artery Disease Classification Using Gradient Boosting Decision Tree Siti Napi'ah; Triando Hamonangan Saragih; Dodon Turianto Nugrahadi; Dwi Kartini; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.331

Abstract

Coronary artery disease, a prevalent type of cardiovascular disease, is a significant contributor to premature mortality globally. Employing the classification of coronary artery disease as an early detection measure can have a substantial impact on reducing death rates caused by this ailment. To investigate this, the Z-Alizadeh dataset, consisting of clinical data from patients afflicted with coronary artery disease, was utilized, encompassing a total of 303 data points that comprise 55 predictive attribute features and 1 target attribute feature. For the purpose of classification, the Gradient Boosting Decision Tree (GBDT) algorithm was chosen, and in addition, a metaheuristic algorithm called monarch butterfly optimization (MBO) was implemented to diminish the number of features. The objective of this study is to compare the performance of GBDT before and after the application of MBO for feature selection. The evaluation of the study's findings involved the utilization of a confusion matrix and the calculation of the area under the curve (AUC). The outcomes demonstrated that GBDT initially attained an accuracy rate of 87.46%, a precision of 83.85%, a recall of 70.37%, and an AUC of 82.09%. Subsequent to the implementation of MBO, the performance of GBDT improved to an accuracy of 90.26%, a precision of 86.82%, a recall of 80.79%, and an AUC of 87.33% with the selection of 31 features. This improvement in performance leads to the conclusion that MBO effectively addresses the feature selection issue within this particular context.
Implementation of SMOTE and whale optimization algorithm on breast cancer classification using backpropagation Erlianita, Noor; Itqan Mazdadi, Muhammad; Saragih, Triando Hamonangan; Reza Faisal, Mohammad; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.334

Abstract

Breast cancer, which is characterized by uncontrolled cell growth, is the primary cause of mortality among women worldwide. The unchecked proliferation of cells leads to the formation of a mass or tumor. Generally, the absence of timely and efficient treatment contributes to this phenomenon. To prevent breast cancer, one of the strategies involves the classification of malignant and non-malignant types. For this particular investigation, the Breast Cancer Wisconsin dataset (original) comprising 699 instances with 11 classes and 1 target attribute was utilized. Synthetic Minority Oversampling (SMOTE) was employed to balance the dataset, with the Backpropagation classification algorithm and the Whale Optimization Algorithm (WOA) serving as optimization techniques. The main objectives of this study were to analyze the impact of the backpropagation method and SMOTE, examine the effect of the backpropagation method in conjunction with WOA, and assess the outcome of using the backpropagation method and SMOTE after incorporating WOA. The evaluation of the study's findings was performed using a confusion matrix and the Area Under the Curve (AUC) metric. The research outcomes based on the application of backpropagation yielded an accuracy rate of 96%, precision of 94%, recall of 95%, and an AUC of 96%. Subsequently, upon implementing SMOTE and WOA, the performance of the backpropagation method improved, resulting in an accuracy rate of 99%, precision of 97%, recall of 97%, and an AUC of 98%. This notable enhancement in performance suggests that the utilization of SMOTE and WOA effectively enhances accuracy. However, it is important to note that the observed improvements are relatively modest in nature.
Regression Algorithms in Predicting the SARS-CoV-2 Replicase Polyprotein 1ab Inhibitor: A Comparative Study Sengkey, Daniel Febrian; Masengi, Angelina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.338

Abstract

Due to its extensive steps and trials, drug discovery is a long and expensive process. In the last decade, as also hard pressed by the COVID-19 pandemic, the screening process could be assisted with the advancement in computational technology including the application of Machine Learning. The classification task in Machine Learning has become one of the major approaches for drug discovery. Unfortunately, this practice uses discretized labels that might lead to the loss of quantitative properties that could be meaningful. Therefore, in this paper, we aim to compare various Machine Learning regression algorithms in predicting inhibitory bioactivity, specifically the IC50 value, with the SARS-CoV-2 Replicase Polyprotein 1ab as the target. With 1,138 non-duplicated data downloaded from the ChEMBL database that was engineered into four dataset variances, 42 regression algorithms were utilized for the prediction. We found that there are computational challenges to the use of regression algorithms in predicting bioactivity, for only a handful and a specific dataset variance that returned valid performance parameters upon testing. The three that yielded the highest counts of valid performance parameters are the Histogram Gradient Boosting Regressor (HGBR), Light Gradient Boosting Machine Regressor (LGBR), and Random Forest Regression (RFR). Further statistical analyses show that there is no significant difference between these three algorithms, except for the time taken for training and testing the model, where the LGBR excels. Therefore, these three algorithms should be primarily considered for the study with the same nature.

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