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INDONESIA
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 199 Documents
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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.
Selection EEG Electrode Positions for Epilepsy Seizure Detection Using Total Power Spectrum and Machine Learning Afifah, Khilda; Istiqomah, Istiqomah; Rizal, Achamd; Nugraha, Ramdhan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.9

Abstract

Detecting epileptic seizures poses significant challenges due to the complex and variable nature of EEG signals, particularly when aiming for implementation in wearable devices. The use of 64-channel EEG electrodes, while comprehensive, is impractical for wearable applications due to their size, cost, and the high computational load required for processing. The use of a single-channel EEG wearable device offers notable advantages, including reduced size and cost, making it more practical and comfortable for continuous monitoring in daily life. Additionally, the lower computational load enhances battery life and allows for real-time data processing, which is critical for timely seizure detection and intervention. This research investigates the detection of epileptic seizures using various machine learning algorithms and the power spectrum feature extraction method from EEG signals, aiming for application in wearable devices with a single-channel electrode. The study applied random forest (RF), K-nearest neighbor (KNN), decision tree (DF), support vector mechine (SVM), and logistic regression algorithms to assess their effectiveness. Results revealed that the power spectrum extraction method notably improved seizure detection accuracy, with RF and KNN achieving 93% and 92% accuracy respectively when using all EEG channels. When limited to a single channel, SVM demonstrated the highest accuracy of 82% with channel 3. These findings underscore the efficacy of the power spectrum method for EEG signal processing, providing significant improvements in accuracy and computational efficiency. The study concludes that the proposed approach is promising for enhancing epileptic seizure detection, suggesting further optimization for real-time application in wearable devices to develop accurate and efficient diagnostic tools.
Exploration of digital filters on cardiac monitor devices equipped with non-invasive blood pressure (NIBP) Nugraha, Priyambada C.; Sumber, Sumber; Muzachim, Zuva; Rabani, Rifqi; Alhaq, Elmira Rofida; Triwiyanto, Triwiyanto; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/edd73780

Abstract

Heart disease is a leading cause of global mortality, making accurate monitoring essential for early detection and prevention of complications. Although heart monitoring technology has advanced, there are still limitations in precisely detecting early symptoms. This study aims to develop a Cardiac Monitor device capable of monitoring patients with heart conditions through three main parameters: electrocardiogram (ECG), phonocardiogram (PCG), and non-invasive blood pressure measurement (NIBP). The system designed in this research integrates digital filters, namely Butterworth (order 2, 4, 8) and Kalman, to enhance the quality of ECG and PCG signals. Additionally, the oscillometric method in non-invasive blood pressure measurement (NIBP) is used as a comparison for blood pressure estimation by analyzing the correlation between the R peak on the ECG signal, pulse transit time (PTT), and the first and second heart sounds (S1, S2) on the PCG signal. Blood pressure estimation is performed using algorithmic calculations to determine the accuracy of the design module in measuring systolic and diastolic pressure. The results indicate that the 8th-order Butterworth filter is more effective in reducing noise in ECG and PCG signals compared to the Kalman filter. The study also finds a significant correlation between the R peak on the ECG and the first heart sound on the PCG. The design module’s blood pressure measurement errors compared to algorithmic estimates are 4.54 ± 4.94 mmHg for systolic pressure and 6.57 ± 3.83 mmHg for diastolic pressure, which are close to the AAMI standard of 5 ± 8 mmHg. These findings highlight the great potential of the developed Cardiac Monitor device in improving early diagnosis accuracy and heart disease management.
MPPT Algorithm Based on Zebra Optimization Algorithm for Solar Panels System with Partial Shading Conditions Eviningsih, Rachma Prilian; Efendi, M. Zaenal; Windarko, Novie Ayub; Nugraha, Anggara Trisna; Prasetya, Farhan Dwi; Abdilla, M. Rafi Damas
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.11

Abstract

The use of solar panels is being pursued as a solution to reduce dependence on fossil fuels. However, solar panels face challenges such as power fluctuations due to environmental conditions and partial shading. To address these issues, an MPPT technique using Zebra Optimization Algorithm (ZOA) has been developed, which integrates foraging behaviour and defensive strategies to achieve GMPP. Simulation testing results show the superiority of ZOA over PSO in achieving GMPP. ZOA's contribution in addressing this problem is to efficiently perform a global search to find the optimal MPP, even under varying partial shading conditions. The algorithm mimics the behaviour of zebras in foraging and defending against predator attacks, enabling a fast solution search process and higher precision. ZOA can overcome the local maxima trap by expanding the search space, allowing solar panels to function close to optimal efficiency even if there is shading on a portion of the module. This improves system stability and performance and reduces energy loss due to partial shading. ZOA achieved a tracking accuracy of 99.99% with an average tracking time of 0.779 seconds and with a power gain of 28.5%, surpassing PSO with an accuracy of 95.18%, an average trcking time of 0.850 seconds with a power gain of 24.68%. In hardware testing, ZOA is also superior to PSO with an average tracking accuracy of 98.96% while PSO is 97.22%. These results underline the outstanding performance of the ZOA algorithm in optimising the power output of solar panels.
Development of TCR-FC Reactive Power Compensation Device with Fuzzy Logic Control in Electric Power Networks Sunarno, Epyk; Prasetyono, Eka; Anggriawan, Dimas Okky; Nugroho, Mochamad Ari Bagus; Eviningsih, Rachma Prilian; Suhariningsih, Suhariningsih; Nugraha, Anggara Trisna; Anggara Trisna Nugraha
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.12

Abstract

Utilization of electrical loads in predominantly inductive single-phase low-voltage power grids, the quality of electrical power becomes poor due to reactive power consumption resulting in a lack of power factor resulting in power loss, voltage drop, and decreased service life of the power grids. equipment. The research on reactive power compensation using TCR-FC aims to make improvements in improving the power factor in single-phase low-voltage electrical networks so that they have flexible control, do not experience excess compensation, have fast dynamic responses, and are space-saving. And can monitor voltage, current, and phase difference parameters through sensor readings to process data mathematically. When using electrical loads, the reactive power value is larger and the power factor is low below 0.85, the system controls the ignition angle of the TRIAC so that the current flowing into the reactor can be controlled by the reactive absorption measure of the fixed capacitor. So, it can improve the power factor. Simulation results can increase the power factor that exceeds the average value of 0.9 by 0.9797 with an error of 0.0288%. Hardware test results can increase the average power factor to exceed 0.9 by 0.9758 with an error of 0.1373%. in conclusion, reactive power compensation devices that use thyristor-controlled reactors and fixed capacitors can be more efficient than capacitor banks.

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