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Eko Didik Widianto
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Center for Biomechanics, Biomaterials, Biomechatronics and Biosignal Processing (CBOIM3S), 5th floor, Lab Terpadu Building, Diponegoro University Jl. Prof Soedarto SH, Tembalang, Semarang, Indonesia
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Journal of Biomedical Science and Bioengineering
Published by Universitas Diponegoro
ISSN : -     EISSN : 27764052     DOI : -
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering
This journal has the scope of all aspects of biomedical science and bioengineering. Its scope also covers medical sciences, signal processing, biomaterial, medical diagnostic tools, ergonomy as well as all related studies.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 1 (2022)" : 5 Documents clear
The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray Elvira Sukma Wahyuni
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.1-6

Abstract

COVID-19 has become a pandemic spread to nearly all countries in the world. This virus has caused many deaths. Screening using a chest X-ray is an alternative to find out positive COVID-19 patients. Chest X-ray is advantageous because every hospital must have an X-ray device so that hospitals do not need additional equipment to detect COVID-19-positive patients. This study aims to compare the machine learning models of Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression to predict COVID-19 positive patients. The stages of the research carried out by this study are the Pre-process stage, feature extraction, and classification. The results showed that the Naïve Bayes classification method got the highest performance with an accuracy of 95.24%.
Electrochemical Detection and Spectrophotometry of Dopamine using Commercial Screen-Printed Electrodes Eunike Thirza Hanita Christian; Basari Basari; Siti Fauziyah Rahman; Yudan Whulanza
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.7-13

Abstract

Lab-on-chip is miniaturized devices integrated into a chip which can run one or several analyses which are usually done in laboratory settings, such as biochemical detection. Dopamine is an important neurotransmitter which regulates hormones, control of movement, emotion, attention, and motivation. Excess, lack, and dysregulation of dopamine could cause numbers of diseases and disorders. The technique used to measure and evaluate dopamine usually are expensive to run, require longer time to run, require some technical qualification to run, require expensive equipments, and some are invasive to do. These are the reasons why a lab-on-chip system is needed to make the detection of dopamine concentration faster, easier, and more portable. This paper studied the accuracy of using electrochemical detection to measure the concentration of liquid specimens of dopamine compared to uv/vis spectrophotometry. Electrochemical detection method named cyclic voltammetry was chosen for this study. The hypothesis for this study is that both peak current (ip) and absorbance positively correlate to concentration, therefore both could be used with minimal error margin. For this study, the peak current (ip) and absorbance of different concentrations of liquid specimen of dopamine are measured, and its regression were observed. It was shown that the concentration of liquid specimens of dopamine is linear to both anodic peak current (ipa) and absorbance. Due to the high R2 values of 0.9883, electrochemical detection could be used and implemented to detect dopamine concentration for application of lab-on-chip, as it is more portable and requires less volume of sample compared to spectrophotometry.
Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands Serly Yuliana; Munawar Riyadi
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.14-17

Abstract

A wheelchair is a tool used to assist people with physical limitations in their legs. The most widely used are standard wheelchairs with a manual operating system by being pushed by hand. However, people with disabilities who have paralysis or suffer from neuromuscular and neurological conditions cannot use this wheelchair. Because of this, in this study focuses on implementing the Brain Computer Interface system to generate five commands to move a wheelchair. There are five important stages in the BCI system, that is signal acquisition, pre-processing, feature extraction, classification, and applications interface. Fast Fourier Transform (FFT) method used to extract brainwave features. The results of FFT are alpha (8-12Hz) and beta (12-30 Hz) waves in the frequency domain. For classifying brain waves into six classes as input commands to drive a DC motor used Support Vector Machine (SVM) method. Based on the test results, the average accuracy of the classification for the whole class reached 93,1%, the accuracy of class 0 (77,3%), class 1 (95,7%), class 2 (97,8%), class 3 (98,0%), and class 4 (97,5%).
Experimental and Numerical Evaluation of Mechanical Properties for Carbon Fiber Reinforced Epoxy LY5052 Composite for Prosthesis Structures Mahfud Ibadi; Yudan Whulanza; Herry Purnomo
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.18-22

Abstract

Carbon Fiber Reinforced Epoxy is one of the materials that is widely used in the manufacture of phosthesis structures. In this study, the carbon fiber used carbon-kyoto type plain weave while the epoxy matrix was LY5052. The maximum stress (s) from the tensile test is 537.15 MPa. Furthermore, tensile test simulation with Finite element analysis simulation using Abaqus software, in the process the selection of mesh through input sizing control determines the accuracy of the results. The simulation results are 523.3 MPa when compared to the experiment the difference is 2.58%
Development of Ergonomics Checklist on Stroke Therapy Aids (Wearable Elbow Exoskeleton) Novie Susanto; Christ Novia Saraswati; Wiwik Budiawan; Rifky Ismail
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.23-29

Abstract

This study aims at building a checklist used for each stage of wearable Elbow Exoskeletons development. The Wearable Elbow Exoskeleton is one of the handstroke therapy aids which was developed by Diponegoro University. The product is still in the form of a prototype so some tests must be carried out before the product is tested on respondents. The test so far is still focused on functionality tests. This study provides a general checklist for ergonomics testing of stroke therapy aid products. The method of the checklist is developed based on an exploratory study and literature review for producing appropriate tests related to product characteristics. There are three iterations of product development and each version repairs the previous version. The implementation of the checklist shows that on the third iteration, the product is aligned with the objective of development and reaches the targeted level of respondents’ satisfaction.

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