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Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 6 Documents
 1 
Search results for , issue "Vol. 6 No. 3 (2024): August" : 6 Documents clear
Web Application Based on MachineLearning for Diabetes Detection usingMicrostrip Resonator and Streamlit Ali , Irsan Taufik; Rahayu, Yusnita; Setiawan, Aris
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.3

Abstract

Globally, the number of people living with diabetes is estimated to continue to increase. Blood needs to be drawn from the body to measure glucose. Invasive methods carry the risk of infection. Estimating the device using non-invasive methods can reduce the risks of invasive procedures. One way is to use machine learning. In this research, a machine learning-based web application was designed using the Artificial Neural Network (ANN) and K-nearest neighbor (KNN) methods to classify a person's diabetes type by adding additional features from the microstrip resonator. The data used is 1000 with 11 features and one output with two labels. The 11 features are gender, age, blood sugar levels, smoking habits, family history of diabetes, height, weight, Body Mass Index (BMI), frequency, return loss, and bandwidth. In the output, two labels are used, namely diabetes and non-diabetes. ANN and KNN both provide high accuracy above 90%. ANN delivers an accuracy of 99.3%, and KNN provides an accuracy of 99.67%. This model will be embedded in web applications created using Streamlit. It is hoped that this web application can simplify and speed up the diagnosis of diabetes so that the disease can be treated quickly and precisely from an early age. This is expected to minimize the dangerous effects of diabetes due to delays in treatment.
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers Paneru, Biplov; Paneru, Bishwash; Shah, Krishna Bikram
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.4

Abstract

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. A thorough investigation of deep learning methods is carried out using the UCI dataset in order to create a reliable and effective model that can correctly identify a range of rice diseases. The suggested transfer learning models performs better at identifying subtle features and complex patterns in the dataset, which results in extremely accurate disease classification. Moreover, the study goes beyond the creation of models by incorporating an intuitive Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer Learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy.  On Renset101 only an accuracy of 54% could be achieved along with only 33% on efficientNetB0. MobileNetV2 trained model was successfully deployed on a tkinter GUI application to make predictions using image or real time video capturing.
Artificial Intelligence (AI) and Extended Reality (XR): A Biomedical Engineering Perspective Investigation Analysis Akhtar, Zarif Bin; Rawol, Ahmed Tajbiul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.5

Abstract

The convergence of Artificial Intelligence (AI) and Extended Reality (XR) has heralded a new era in the field of Biomedical Engineering, offering unprecedented avenues for innovation, diagnostics, treatment, and education. This research delves into the symbiotic relationship between AI and XR, unraveling their collective potential to revolutionize healthcare practices. AI, characterized by its ability to learn and adapt, has transcended its role within data analysis to become an indispensable tool in healthcare. Through advanced algorithms, AI can predict disease patterns, enhance medical imaging, and optimize treatment protocols. On the other hand, XR technologies, encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), immerse users in virtual environments, facilitating interactive and experiential learning and treatment methods. This research focuses on the study that examines with the integration of AI and XR in biomedical applications, elucidating their role in diagnosis, treatment, and training. AI-driven image analysis augments medical imaging, expediting disease identification and tracking treatment progress. XR, through its immersive nature, empowers surgeons with detailed anatomical insights during procedures and aids in rehabilitation through engaging simulations. The synergistic marriage of AI and XR also redefines medical education by offering immersive training experiences to healthcare practitioners and bridging the gap between theory and practice. Furthermore, ethical considerations and challenges emerge as these technologies evolve. Privacy concerns, data security, and the need for regulatory frameworks are paramount in this dynamic landscape. Striking the right balance between innovation and patient safety remains an imperative task. In the context of this research, the fusion of AI and XR from a biomedical engineering perspective holds the potential to revolutionize healthcare. As AI refines diagnostics and treatment strategies, XR provides a tangible platform for immersive experiences that enhance training and therapeutic interventions. This research navigates the landscape of this transformative convergence, shedding light on its profound implications for Biomedical Engineering and the well-being of patients worldwide.
Desain 3 Dimension Baby Incubator Using SketchUp Application Based On Indonesia National Standars Fatwasauri, Icha; Zaini, Anshari Ahmad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.6

Abstract

Premature birth is one of the causes of perinatal death. Premature babies have a higher risk of death because babies find it difficult to adapt to life outside the womb due to immature organ systems of the baby's body. Medical devices used to maintain babies born prematurely are called incubators. In making incubators, usually before being made, you must try making a prototype first. In addition, it must also be simulated so that the results of the incubator are up to standard. Various studies related to simulators have been conducted, such as using matlab. This takes a relatively long time to learn. So the author designed the 3-dimensional design of the incubator is a cutting. Sketchup is easier to learn for beginners, this software is also free. The research method that researchers use is research and development. Researchers carry out several stages, namely literature study, initial design, 3-dimensional design, validation, and discussion. From the results of the design that has been made and given a questionnaire to material experts, a value of 67% or said to be feasible. Validation is also carried out by direct measurement and the results are not much different
Early Detection of Stunting Based on Feature Engineering Approach Ahadi Ningrum, Ayu; Ikawati, Yunia
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.7

Abstract

The stunting problem in Indonesia is still an extensive issue for the government. Around 22% of cases of stunting affect brain development, resulting in reduced intellectual capacity and permanent disruption of the structure and function of nerves and brain cells. This research describes early detection of stunting using an  feature selection approach. So, datasets related to stunting are valuable in providing complete insight or information in detecting early symptoms of stunting in toddlers. Machine learning modelling for early detection of stunting in this study shows that of the 14 features predicting the value of detecting stunting, only seven features are influential based on their correlation values. When testing continues using Machine Learning algorithms with various variants, the Multilayer Perceptron algorithm can produce an accuracy value of 98%.
Identification of Blood Sugar Based on Non-invasive Measurements UsingPhotoplethysmography Method Signal Decoding in Diabetics in Tasikmalaya Badriah, Siti; Bahtiar, Yanyan; Cahyati, Peni; Andang, Asep; Fathurrohman, Fahmi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.8

Abstract

Non-invasive blood sugar testing tools are now in great demand among the public. This research aims to develop a non-invasive blood sugar examination tool with a Photopletysmograph signal decoding system based on data acquisition from a wearable sensor device on the wrist. The tool used for data acquisition uses Near Infra Red Light-emitting Diode NIR LED 880 nm, 660 nm and 537 nm. This acquisition transfers signals from the body via a photodiode to a PC Personal Computer for processing using Matlab. The method used in this research is to look for the height of the systolic and diastolic amplitude of the Photopletysmograph PPG signal and use the peak-to-peak voltage Vpp to implement Beer Lambert's law. This method was tested on 54 people, randomly aged 26-89 years, with normal blood sugar to high blood sugar. The results show an error of 18.37% from the gold standard. In conclusion, the use of the PPG signal decoding method has proven to be significant in producing blood sugar values. Furthermore, this method was developed for real-time remote measurements via wearable sensor devices.

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