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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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Fuzzy Logic Temperature Control on Blood Warmer Equipped with Patient Temperature and Blood Temperature Hafizh, Andika Wahyu Nur; Hamzah, Torib; Syaifudin , Syaifudin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ha2p2x68

Abstract

Body temperature in humans varies greatly depending on the location where the reading is taken. Normal core body temperature in humans is maintained by the hypothalamus and usually ranges from 36.5°C to 37.5°C. One of the causes of the failure of Too high or too low of a temperature during the blood transfusion procedure may cause blood to freeze or get damaged, both of which can be fatal to humans, therefore the purpose of this tool is to lower blood temperature admission to the patient can be achieved so that there is no temperature drop or temperature drop and so that the blood is not too hot because it can cause damage to red blood cells. This study uses the DS18B20 Sensor to control the heater with PID and Fuzzy controls, the MLX90614 Sensor to adjust the temperature according to the patient's body temperature and the Optocoupler Sensor as an indicator when fluids run out. Previous studies have not used the MLX90614 sensor to detect patient body temperature, have not used TFT Nextion and have not used Fuzzy controls. This Fuzzy control is used as a heater control which then the results are displayed on the Nextion TFT. The results of this study obtained the highest error value of 0.09 with an average error value of 0.04 and obtained the highest overshoot value of 0.8. From the results of the above study it can be concluded that by using the Fuzzy control the response time is slower with a larger overshoot. In the creation of this tool, the benefits that can be derived for the community are facilitating the monitoring of patient temperature and blood temperature during blood transfusions using the Blood Warmer device. The device is also equipped with sensors to detect patient and blood temperatures, and it comes with a Nextion TFT display. Therefore, this device is crucial in assisting the community in performing Blood Transfusions.
Spo2 Analysis on Development of IoT-Based Lung Function and Spo2 Measuring Device Shavira, Nadya; Ariswati, Her Gumiwang; Hamzah, Torib
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/dx15cf29

Abstract

Pulmonary dysfunction is a widespread issue, particularly in developing nations. It encompasses restrictive, obstructive, and mixed pulmonary function disorders that lead to a decrease in vital lung capacity, an increase in functional residual capacity, and a decline in blood oxygen concentration and saturation. This study aims to combine oximetry and spirometry into a single device, using the Internet of Things (IoT) technology to display results via a smartphone app. The focus is on analyzing oxygen saturation, with normal levels ranging from 96% to 100% in adults, alongside a heart rate of 60-100 beats per minute. The MAX30102 sensor measures oxygen saturation, and the Arduino Pro Mini and D1 Mini ESP32 microcontrollers process data. The Android-based app, developed using Kodular platform, integrates a MySQL database and connects to the device module via Wi-Fi. Ten respondents underwent five measurements, revealing an average error of ±0.88% for oxygen saturation (SpO2) and ±2.82% for heart rate measurements. The average data loss rate during transmission was ±0.66% for SpO2 and ±0.89% for heart rate. These findings highlight existing errors in the module. The research aims to facilitate remote health monitoring for healthcare professionals, improving accessibility and healthcare provision
Design and Development of an IoT-based Pulmonary Function Monitoring Device of FVC and FEV1 for Children with Bronchial Asthma Luthfiyah, Sari
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/wew4nm63

Abstract

Health information technology plays a crucial role in managing the healthcare of patients and their families during illness. One of the frequently encountered diseases is Asthma, a chronic inflammatory disorder of the respiratory tract that is reversible and fluctuating, capable of causing exacerbations with mild to severe symptoms and even death. The objective of this research is to develop a device to facilitate the monitoring and input of data regarding pulmonary volume measurements (spirometry). The sensors used for measuring pulmonary volume are the flow turbine sensor, while the SpO2 sensor used is the MAX30102. The data obtained from the sensor measurements will be processed on the ESP32. A health monitoring application is created using Kodular software, which incorporates a MySQL database for data storage. Furthermore, the examination results can be accessed through an Android application on a tablet or smartphone. The results obtained from this research indicate an error value of 8.78% for FVC, 14% for FEV1, and a FEV1/FVC ratio of 4.6%, with zero data loss. It is expected that the spirometer with Internet of Things (IoT) capabilities will be implemented, as monitoring can be easily conducted anywhere. The portable design will facilitate future examinations. The implications of this study are that it obtains information about individual variability in lung function measurement, the public can better understand the importance of respiratory health monitoring, as well as support the development of better medical technology to improve lung disease diagnosis and management and improve spirometer technology.
A Deep Learning Application Built with Tkinter for Waste Recycling and Recommending Solutions Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Shah, Krishna Bikram; Poudyal, Khem Narayan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/j3hrme70

Abstract

This paper presents a novel PyTorch model integrated with a Tkinter-based Recycling Recommendation Application to address the pressing issue of waste management. Our waste prediction and classification model achieve high precision by leveraging advanced machine learning techniques and a large dataset. We improve classification accuracy and speed using pre-trained models and transfer learning, which is critical for effective waste management. The accompanying Tkinter application improves recycling recommendations by allowing users to input information through an intuitive interface. Our PyTorch model has exceptional accuracy, scoring 99% on the training set and approximately 96% on validation, which is supported by robust stratified cross-validation. This fusion of cutting-edge machine learning and user-centered design represents a significant step toward more efficient waste management and environmentally friendly waste disposal practices. The system's potential for widespread adoption is highlighted by its accuracy in categorizing various waste items and providing tailored solutions, resulting in a positive environmental impact.
A Fuzzy Logic Approach to Enhance GPS Accuracy for Blood Cooler Box Tracking Hariyono, Muhammad Akbar; Marwanto, Arief; Alifah, Suryani
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/fbsvte54

Abstract

An innovative technological development, the position tracking system uses latitude and longitude coordinates to determine the GPS's location. The covid-19 pandemic has caused the demand for cool box delivery for convalescent blood plasma to increase significantly. The obstacle in the field is the hospital's position and patient rooms far from the reach convalescent donors, so they require a cool box for delivery. The problem is tracking the cool box accurately and precisely so that it can be monitored by the hospital properly. For this reason, it is necessary to increase the accuracy of the GPS position in the cool box by using fuzzy logic algorithms The Ublox NEO-6M is one GPS module that can be utilized for navigation. This module uses UART connection using the NMEA 0183 protocol and has an accuracy level of around 2.5 meters to 10 meters. In this research, validation of the accuracy of the GPS coordinate position was carried out on a Blood Cool Box device which was designed using the fuzzy logic method. The Sugeno method fuzzy logic algorithm is used to validate the accuracy of GPS coordinate positions based on latitude and longitude obtained from the GPS sensor module. The test results show a Mean Absolute Percent Error (MAPE) value of 21.66% which can be concluded that the Sugeno fuzzy logic method algorithm has forecasting model capabilities that are suitable for use as a validation method for testing GPS position coordinates.
Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG) Rakhma, Ukhti Alifah Aulia; Loniza, Erika; Kartika, Wisnu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/p66tf905

Abstract

Background this prototype is intended for post-stroke patients experiencing disabilities in their daily activities, particularly in communication with others. The challenge they face is difficulty in communication, leading to a diminished quality of life for post-stroke patients. The purpose of innovating this communication aid prototype is to facilitate communication between post-stroke patients and caregivers. The method employed in the post-stroke communication aid prototype utilizes Electrooculography (EOG) signals generated from eye muscle movements during eye gazes, captured by the MaM Sense sensor. The variation in Analog-to-Digital Converter (ADC) values in the MaM Sense sensor is exploited to produce various forms of EOG signals. The resultant command signals from this method are processed by a microcontroller and displayed on a 20 x 4 Character LCD. Testing was conducted on 9 healthy individuals, comprising 5 males and 4 females. To ensure the prototype's functionality, testing was also performed on 1 post-stroke patient. The success rate of MaM Sense sensor readings was 80.5% for the 4 communication modes employed, involving 4 eye gaze movements: right gaze, left gaze, upward gaze, and downward gaze. Thus, the post-stroke communication aid prototype proves effective in assisting communication for post-stroke patients and aiding caregivers in understanding the patients' desires. In the future, a wireless system may be implemented for the acquisition of EOG signals attached to the face to minimize the use of cables.
Thermal Image Classification of Autistic Children Using Res-Net Architecture Ahmadiar, Ahmadiar; Melinda, Melinda; Muthiah, Zharifah; Zainal, Zulfan; Mina Rizky, Muharratul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/365fkd59

Abstract

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.
LoRA-LoRaWAN Communication Multinode for 3D Localization in Coastal Environment Musayyanah, Musayyanah; Pauladie Susanto; Pradita Maulidya Efendi; Charisma Dimas Affandi; Kristin Lebdaningrum; Theodorus Visser Inulima
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/xv8hwc75

Abstract

The application of LoRAWAN on Internet of Things (IoT) technology is  to communicate in real time and accommodate data from many nodes based on device addresses. LoRAWAN device communication is able to reach distances of up to kilometers with low cost, compared to high-frequency cellular communication currently installed on the coast. This application can be done on LoRA devices to forward the results of three-dimensional localization based on signal strength. This research was  to conduct three-dimensional localization based on signal strength from three LoRa End Nodes (EN) to four LoRa Anchor Nodes (AN), then forwarded to the server to be displayed on the Datacakes application. Localization begins with a path loss model analysis to determine the path loss coefficient. The localization results were  in the form of data packets consisting of longitude, latitude, and altitude position parameters and the results of the EN to AN distance conversion. The data packet was  forwarded to the The Things Networks (TNN) server with Over The Air Activation (OTAA) activation mode. Root Mean Square Error (RMSE) analysis of the localization results for EN1 was  169.35 meters, EN2 was  395.08 meters and EN3 was  183.24 meters. The localization data packets were  forwarded to the cloud server via the GW device. Analysis of GW communication with EN is shown by the Packet Error Ratio (PER), Air Time (AT), and latency parameters. The smallest PER results, fastest AT and lowest latency were  obtained from GW communication with EN2, where the position of EN2 was  closest to GW among the other ENs.
Bitcoin Mining Hardware Profitability Prediction Using Categorical Boosting and Extreme Gradient Boosting Algorithms Dimas Satria Prayoga; Puspita Sari, Anggraini; Junaidi, Achmad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/9xb2dz14

Abstract

Cryptocurrencies, especially Bitcoin, have gained global recognition, with mining being one of its most interesting aspects. This is especially important in the context where only a few types of bitcoin mining rigs are expected to operate profitably. On the other hand, in the field of machine learning, there are widely used algorithms, namely Extreme Gradient Boosting (XGBoost), which is known for its effectiveness, and Categorical Boosting (CatBoost), which excels in handling categorical data. This study aims to combine the performance of CatBoost and XGBoost using the Ridge Regression technique in predicting a case study that is not often encountered, namely predicting the profitability of Bitcoin mining hardware. The main steps include collecting data from reliable sources, preprocessing the data to ensure compatibility, feature selection to select the most relevant features, building a prediction model using the preprocessed data set, and then training and testing both models to evaluate their predictive accuracy. The evaluation metrics on the test data reveal the performance of CatBoost, XGBoost, and the CatBoost-XGBoost. CatBoost demonstrates a training time of 3.35 seconds with a MAPE of 15.67% and an RMSE of 0.1733. In comparison, XGBoost has a longer training time of 5.27 seconds but achieves a significantly lower MAPE of 6.49% and an RMSE of 0.1737. Meanwhile, the CatBoost-XGBoost, with the longest training time of 6.84 seconds, delivers a competitive MAPE of 6.57% and the lowest RMSE of 0.1696 among the three approaches. These results highlight that while XGBoost and CatBoost meta model outperform CatBoost in terms of accuracy, the Ridge meta model provides slightly better overall predictive performance based on RMSE.
Examining the Relationship between Water-Equivalent Diameter (Dw) and Body Mass in Breast Cancer Patients Nurhanivah, Devi; Ramdhani, Saumi Zikriani; Bilqis, Ayesha
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/3g7sep81

Abstract

Breast cancer is the most prevalent cancer worldwide, necessitating precise imaging techniques for effective treatment planning. This study aims to analyze the Water-Equivalent Diameter (Dw) in breast cancer patients using Computed Tomography (CT) and to investigate its relationship with patient body mass. Medical imaging data from 30 breast cancer patients, aged 23-66 years, was reviewed to calculate Dw using three different methods: contour ROI, elliptical ROI, and without ROI. The results showed average Dw values of 28.68 cm for contour ROI, 29.184 cm for elliptical ROI, and 30.255 cm without ROI. This indicates that contour ROI yields the smallest Dw due to its focus on cancerous areas. Furthermore, a positive linear correlation between Dw and body mass was established, with an R² value of 0.7743. This suggests that larger body mass leads to increased Dw values. This study emphasizes the importance of considering ROI selection and highlights the significant impact of patient body mass on Dw. This is crucial for optimizing radiation exposure in breast cancer treatment.

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