Indonesian Journal of electronics, electromedical engineering, and medical informatics <div align="justify">The Indonesian &nbsp;Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) is a peer-reviewed periodical scientific journal aimed at publishing research results of the Journal focus areas. The Journal is published by the Department of Electromedical Engineering, Health Polytechnic of Surabaya, Ministry of Health Indonesia. The role of the Journal is to facilitate contacts between research centers and the industry. The aspiration of the Editors is to publish high-quality scientific professional papers presenting works of significant scientific teams, experienced and well-established authors as well as postgraduate students and beginning researchers. All articles are subject to anonymous review processes by at least two independent expert reviewers prior to publishing on the Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics website.</div> Department of electromedical engineering, Health Polytechnic of Surabaya, Ministry of Health Indonesia en-US Indonesian Journal of electronics, electromedical engineering, and medical informatics 2656-8624 <p><strong>Authors who publish with this journal agree to the following terms:</strong></p> <ol> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International <a title="CC BY SA" href="" target="_blank" rel="noopener">(CC BY-SA 4.0)</a>&nbsp;&nbsp;that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See&nbsp;<a href="" target="_new">The Effect of Open Access</a>).</li> </ol> Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method <p>At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method. &nbsp;The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.</p> Annisa Puspa Kirana Adhitya Bhawiyuga ##submission.copyrightStatement## 2021-02-21 2021-02-21 3 1 1 8 10.35882/ijeeemi.v3i1.1 Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model <p>Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm. &nbsp;</p> Tsehay Admassu Assegie ##submission.copyrightStatement## 2021-02-21 2021-02-21 3 1 9 14 10.35882/ijeeemi.v3i1.2 Communication Coroutines For Parallel Program Using DW26010 Many Core Processor <p>Communication between parallel programs is an indispensable part of parallel computing. SW26010 is a heterogeneous many-core processor used to build the Sunway Taihu Light supercomputer, which is well suited for parallel computing. There is the designing and implementing a coroutine scheduling system on the SW26010 processor to improve its concurrency, it is very important and necessary to achieve communication between coroutines for the coroutine scheduling system in advance. Therefore, this paper proposes a communication system for data and information exchange between coroutines on SW26010 processor, which contains the following parts. The designing and implementation a producer-consumer mode channel communication based on ring buffer, and it designs synchronization mechanism for condition of multi-producer and multi-consumer based on the different atomic operation on MPE (management processing element) and CPE (computing processing element) of SW26010. There is also the designing of a wake-up mechanism between the producer and the consumer, which reduces the waiting of the program for communication. The testing and analysis of the performance of channel in different numbers of producers and consumers, draw the conclusion that when the number of producers and consumers increases, the channel performance will decrease.</p> Ajit Singh ##submission.copyrightStatement## 2021-02-22 2021-02-22 3 1 15 20 10.35882/ijeeemi.v3i1.3 Development of a Low-Cost and Effisient ECG devices with IIR Digital Filter Design <table width="696"> <tbody> <tr> <td width="539"> <p><em>Measurement of biosignals such as electrocardiograph has the interpretation of noise from other signals. The noise can interfere with the measurement of the heart signal and make the measurement inaccurate, so the purpose of this study is to make a 6-Lead Electrocardiogram module with an Arduino-Based Digital Filter. By using a digital filter. The contribution of this research is the use of digital filters to eliminate noise in electrocardiograph signals. This research uses Infinite Impulse Filter digital filters such as Butterworth, Chebyshev I, Chebyshev II, and Elliptic in order 2, 4, 6, 8, and 10. The study was conducted by providing input from the Function Generator on Arduino which has been applied digital filters with Frequency with 0.5Hz – 100Hz cut-off. The instrument is compared with a factory electrocardiograph. Filter measurements using 460 input data. Butterworth filter with the greatest emphasis on order 8 frequency 0.5Hz produces an emphasis of -5.74298158 dB and a frequency of 100Hz produces an emphasis of -5.93529424 dB. The Chebyshev I filter has the greatest emphasis on order 6 frequency 0.5Hz producing an emphasis of -3.27104076 dB and on order 8 frequency 100Hz producing an emphasis of -5.08730424 dB. Chebyshev II filter the biggest emphasis on the order of frequency 0.5Hz produces a suppression of -44,66011104 dB and 80Hz frequency produces a suppression of -37,3653957 dB. Elliptic filters the greatest emphasis on order 6 frequency 0.5Hz produces an emphasis on -1.55429354 dB and 100Hz frequency on order 8 produces an emphasis on -2.2849115 dB. The results showed that what was appropriate with the cut-off frequency was the Butterworth order 8 filter which was suitable for the application of the Electrocardiograph signal filter because it had bandwidth that suppressed the signal outside the cut-off frequency. The results of this study can be implemented on a 6-Lead ECG module to eliminate noise or interference when tapping ECG signals.</em></p> </td> </tr> </tbody> </table> Rizki Aulia Rachman I Dewa Gede Hari Wisana Priyambada Cahya Nugraha ##submission.copyrightStatement## 2021-02-22 2021-02-22 3 1 21 28 10.35882/ijeeemi.v3i1.4 Design an Occlusion Calibrator using XGZP6887 and Servo Motor MG966R as a Simulator <p>A foreign fluid that enters the patient can cause some bodily reactions including infection, air embolism and blood clot. Side effects given will be fatal to the body, one of which occurs the blockage of the capillary vessels in the heart that can cause heart attack to stroke. The purpose of this research is to design a tool that can be used to measure maximum pressure as a form of the calibration of the syringe pump and infusion pump. The contribution of this research is that the system can simulate the presence of blockages in fluid flow and detect large pressure values detected by the Under Test Unit (UUT) with a motor peerround system that opens/closes fluid flow. Servo Motor MG966R simulate the presence of blockage with constant motor degree until the alarm UUT reads, then Sensor XGZP6887 detects the pressure generated by the blockage and processed by the microcontroller and displayed on the LCD display of the character. This study resulted in a maximum pressure average value of 7.12 Psi. The results showed that data retrieval had an error value of -0.12. This research can be implemented to perform pressure measurements on the syringe pump or infusion pump.</p> Rizki Auliya Syaifudin Syaifudin Liliek Soetjiatie ##submission.copyrightStatement## 2021-02-22 2021-02-22 3 1 29 33 10.35882/ijeeemi.v3i1.5 Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters <p>Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.ECG, Respiration Rate, Filter</p> Gede Aditya Mahendra Oka Andjar Pudji ##submission.copyrightStatement## 2021-02-22 2021-02-22 3 1 34 38 10.35882/ijeeemi.v3i1.6