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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 7 Documents
 1 
Search results for , issue "Vol 2 No 2 (2020): July" : 7 Documents clear
A Two-Mode Digital Pressure Meter Equipped With An Automatic Leak Test Using MPX5050gp And MPXv4115vc6u Sensors Fita Florensa Rooswita; Triana Rahmawati; Syaifudin Syaifudin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.7

Abstract

The calibration process aims to guarantee that a measurement device is to follow the established standards. The purpose of this study is to design an automatic leak test for digital pressure meter in which the function of this device is to measure pressure on the sphygmomanometer and suction pump or other devices that use pressure parameters for measurement. This study used the Arduino as a control system, and to process the analog data into digital. Signal conditioning, based on the amplifier circuit, was also applied for the MPX5050GP and MPXV4115VC6U sensor. The proposed design used a 4x20 liquid crystal display to show the parameters in this design ware also equipped with a selector of mmHg or kPa units. The result shows that the fluctuating resolution is 0.25 mmHg. In this design, an automatic leak feature was also equipped for the sphygmomanometer. The results obtained an average error of 7.3 mmHg for sphygmomanometers. On the other hand, the suction pumps have an accuracy of less than 1.5 kPa. From these results, it was concluded that this design could be used for the measurement of devices that use positive pressure and negative pressure
Development of Incubator Analyzer Using Personal Computer Equiped With Measurement Certificate Laily Nurrohmah; Dwi Herry Andayani; Andjar Pudji
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.6

Abstract

Baby incubators are used for premature babies when babies are born prematurely. In order to ensure the accuracy of medical devices, periodic tests and controls are needed, which aim to reduce the risk of measurement. The baby incubator can be tested with a calibration device that is used to calibrate temperature, noise, humidity, and airflow so that the conditions remain stable and within normal limits. The purpose of this study is to develop a calibrator device based on a computer to measure noise and airflow parameters. The standard incubator analyzer is not equipped with a computer interfacing. Furthermore, it needs data processing via Excel. Therefore, in this study, an incubator analyzer device is proposed, which has four parameters to measure, namely, temperature, noise, humidity, and airflow. The main part of this design is the Atmega328 Microcontroller, in which the function is used as a data processor, equipped with Bluetooth communication and data storage. Furthermore, the output will be displayed in a computer unit. In this study, the noise was measured using analog sound Sensor V2; and have the most significant error at 37oC setting temperature that is equal to 0.17%. At the same time, the airflow parameter measured using an airflow sensor, type D6F-V3A01. Based on the measurement, the error was 0.5% at a temperature setting of 36oC and 37oC. The use of displays on personal computers and data processing using Excel allows users to monitor calibration and data processing. The feasibility of this device is proven. Therefore, this design can be used for baby incubator calibration.
A Low-Cost Transcutaneous Electrical Nerve Stimulation Measuring Device Using Frequency-to-Voltage and Current-to-Voltage Alfita Kurniawati; Torib Hamzah; Tri Bowo Indrato
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.5

Abstract

The use of transcutaneous electrical nerve stimulation (TENS) therapeutic devices to reduce the complexity of the patients continuing can cause an increase in the performance of the tool. The purpose of this study is to design a tool to calibrate TENS. The contribution of this study is the ease of users when performing TENS calibration because it can display the shape of the signal, the frequency value in units of Hz, as well as the current value in units of mA directly. To match the frequency and current according to the position of the red electrode cable, it must be higher than the position of the black electrode cable. The frequency-to-voltage that is changed then entered is converted into a voltage to be processed using Arduino. Then also with the current-to-voltage, which changes the inrush current and then is converted into a voltage to be processed using Arduino. The results showed that the frequency values ​​in all settings had an average error of 0.018, while the average error of the current in all settings was 0.25. At the frequency, a measurement obtained highest uncertainty value of UA is 1.6, UB is 0, and the highest U95 is 6.88 while in the current measurement obtained, the highest uncertainty value of UA is 0.19, UB is 0, and highest U95 is 0.392. The results of this study can be applied to the field of calibration, specifically the TENS therapy instrument calibration.
Development of Incubator Analyzer Based on Computer with Temperature And Humidity Parameters Syarifatul Ainiyah; Dwi Herry Andayani; Andjar Pundji; Triwiyanto Triwiyanto; M Shaib
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.3

Abstract

By opening and closing an infant incubator window during calibration, it can cause temperature leaks, such as a decrease in the incubator temperature. The purpose of this study is to develop an incubator analyzer, in which the data can be recorded to a computer for temperature and humidity parameters based on Bluetooth communication. Whereas for a non-computer displayed, the information is shown on a 20x4 LCD with SD Card storage. The contribution of this study is to calibrate baby incubators without a decrease in temperature, and also, the system can monitor the data collection at a maximum distance of 10 meters. In order to avoid decreasing in temperature, the module is displayed on the Personal Computer and storage on the SD Card. Incubator Analyzer is designed to simplify and facilitate calibration with temperature parameters at 5 points using a DS18B20 sensor, mat temperature using a K type thermocouple and humidity using a DHT22 sensor. In the temperature setting of 34 C and 36 C, the average error result is -4.87% for DS18B20, -7.39% error for mattress temperature, and -24.80% for humidity sensor. Data generated from comparisons using the INCU II test conclude that the measurement results between modules and standard devices have significant differences in values. The results of this study can be implemented on baby incubators to increase the appropriateness of the device.
Water-Bath Calibration Device with Data Storage Using Six Thermocouple Sensor Yanti Kusumawardani; Endang Dian Setioningsih; Dyah Titisari
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.2

Abstract

Uneven temperature distribution in the water-bath chamber can cause the temperature conditions in the chamber are not the same. Temperature, humidity, atmospheric pressure, and dust particles are the main factors that adversely affect the accuracy of the water bath's temperature. Therefore, the purpose of this study is to design a calibration device for water-bath with six-channel temperature sensors. In this study, the system able to detect temperatures at each point. The K-type thermocouple sensor is used to detect the temperature at each chamber point with the help of the MAX6675 module as a signal conditioning amplifier. The sensor readings will be displayed on a personal computer using a USB cable, and the sensor readings can be stored on a personal computer in the TXT format so that the data can be reprocessed using Microsoft Excel for further calibration purposes. This study aims to facilitate the calibration process and the processing of calibration data. Based on the obtained measurements, a temperature error for 40 ° C channel one 1.4 %, channel two 1.8%, channel three 0.4%, channel four 0.2%, channel five 0.2% and channel 6 0.2%. Furthermore, the accuracy for temperature setting of 50 ° C for channel one 2.25%, channel two 2.26%, channel three 2.00%, channel four 2.44%, channel five 2% and channel six 1.6%. Moreover, the accuracy for setting temperature 60 ° C for channel one is 0.3%, channel two 0.6%, channel three 0.5%, channel four 1.5%, channel five 2% and channel six 1.8%. Based on the test results, this design has the lowest error of 0.2% and the highest error of 2.44%. The results of this research can be implemented as a water bath calibrator device to maintain the temperature stability of the instrument.
Design a Vital Sign Monitor for Body Temperature (Axilla) and Oxymetry Parameters Mohamad Adam Firdaus; Andjar Pudji; Muhammad Ridha Mak'ruf
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.4

Abstract

In most hospitals, nurses routinely calculate and document primary vital signs for all patients 2-3 times per day to get information. Vital sign monitor is made for medical devices that can diagnose patients who need intensive care to determine patient needs. Some parameters were used oxygen saturation (SPO2), and body temperature. Therefore, the purpose of this study is to develop a vital sign monitor to record body temperature and oxygen saturation. This makes additional tasks are very important to be evaluated for medical staff and equipment manufacturers. This evaluation is needed to get the real condition of the patient. With the large number of patients who need evaluation, it is not possible to see the condition of some medical workers who work. This medical service is expected to reduce the workload of nurses with doctors and improve the quality of patient care. The great demand for these devices, mostly in intensive hospital rooms, is the basis for researching the output of data from multiple vital sensor monitor monitors to obtain accurate and precise outputs. The output of the two sensors is processed by Arduino Mega2560 and requested on a 5 inch TFT LCD in the form of body temperature and oxygen saturation. Comparison of module results with standard measuring instruments calibrated to reference this module is used for accurate and precise results. According to the assessment and reversing tool data with the dressing tool, the highest error value is 1%. With a maximum permitted permission of 5%.
Comparison of Machine Learning Algorithm For Urine Glucose Level Classification Using Side-Polished Fiber Sensor Riky Tri Yunardi; Retna Apsari; Moh Yasin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 2 No 2 (2020): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v2i2.1

Abstract

Urine glucose levels can be used to determine if glucose levels in the human body are too high, which may be a sign of diabetes. A non-invasive urine glucose classification model was conducted by using of the color of urine after benedict reaction to measure the level of glucose. The aim of this study is to classification urine glucose levels from a side-polished fiber sensor performed by using machine learning algorithms to get the best algorithm performance. By removing the coating and cladding this sensor is made of a polymer optical fiber. The measurement is focused on changes in the cladding refractive index which affects the amount of light transmitted. The machine learning system has been implemented using the Naïve Bayes Classifier, k-Nearest Neighbor Classifier, Logistic Regression, Random Forest, Artificial Neural Networks and Support Vector Machine. The measurement data on samples were collected from previous studies of a total of 120 urine samples for testing in this study. The results of the experiments performed with k-fold cross validation show that the neural network gets the accuracy results of 96.7%, the value of precision 0.967, recall 0.967, and F1-Measure 0.967. With cross validation leave-one-out, the experimental results show the classification algorithm with the best accuracy value that is at the random forest and artificial neural networks 0.975, precision 0.975, recall 0975, and F1-Measure 0.975. While the ANN algorithm is superior in achieving an accuracy value of 98.6%. Therefore, artificial neural networks are the best method for classifying glucose levels in the human body for fasting and postprandial urine tests.

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