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dc.contributor.authorFIBRIANI, Ike
dc.contributor.authorWIDJONARKO, Widjonarko
dc.contributor.authorPRASETYO, Aris
dc.contributor.authorRAHARJO, Angga Mardro
dc.contributor.authorIRAWAN, Dasapta Erwin
dc.date.accessioned2020-11-06T02:54:56Z
dc.date.available2020-11-06T02:54:56Z
dc.date.issued2020-10-07
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/101734
dc.description.abstractThe COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Intelligent Engineering and Systems, Vol.13, No.6, 2020en_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectEnsembleen_US
dc.subjectMajority voteen_US
dc.subjectX-Rayen_US
dc.titleMulti Deep Learning to Diagnose COVID-19 in Lung X-Ray Images with Majority Vote Techniqueen_US
dc.typeArticleen_US
dc.identifier.kodeprodiKODEPRODI1910201#Teknik Elektro
dc.identifier.kodeprodiKODEPRODI2010101#Pendidikan Dokter
dc.identifier.nidnNIDN0707028002
dc.identifier.nidnNIDN0008097102
dc.identifier.nidnNIDN03026904
dc.identifier.nidnNIDN0005038007


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