Article Per Year (5 Year)
p-Index From 2019 - 2024
2.959
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics WARTA Emitor: Jurnal Teknik Elektro Jurnal INSYPRO (Information System and Processing) Jurnal Mitra Manajemen INOVTEK Polbeng - Seri Informatika KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Informatika (JUTIF) International Journal of Social Science, Education, Communication and Economics Abdi Teknoyasa
Azizah Fatmawati
Program Studi Informatika Universitas Muhammadiyah Surakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Comparison of transfer learning method for COVID-19 detection using convolution neural network Helmi Imaduddin; Fiddin Yusfida Ala; Azizah Fatmawati; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3525

Abstract

Currently, one of the most dangerous diseases is Coronavirus disease 2019 (COVID-19). COVID-19 is a threat to the whole world, and almost all countries are experiencing the COVID-19 pandemic, including Indonesia. Various ways to detect COVID-19 sufferers have been carried out, such as swab tests, rapid tests, and antigens. One way that can be done to detect COVID-19 infection is to look at X-ray images of the patient's lungs because someone infected with COVID-19 has a different lung shape from normal people. Many studies have been carried out to detect COVID-19, using either machine learning (ML) or deep learning (DL). In this study, we propose to use transfer learning as an extraction feature in the classification of the covid dataset. The study was conducted four times using four different methods, namely ResNet 50, MobileNet V2, Inception V3, and DensNet-201. After experimenting, we compared the results to find out which method has the best results in detecting COVID-19. From this research, it was found that the ResNet 50 model has the best results with 92.3% accuracy, 93% precision, 93% F1-Score, 99% sensitivity, and 90.7% specificity.
Co-Authors Annisa Dwi Rahmawati Brian Aditya Hermansyah Dwi Pratiwi Putri Fafah Hanifah Fiddin Yusfida Ala Fitri, Khilyatin Ulin Hanifah, Fafah Helmi Imaduddin Heny Novita Sari Herlina Widiastuti Khilyatin Ulin Fitri Mohammad Aziz Nurritanto Rahmawati, Annisa Dwi Rama Elian Zuldi Reyhannisa Erico Dwi Reyhannisa Erico Dwi Ramadhana Rina Kurniasari Rina Kurniasari Rindhitya Arfiananda Vintan Kirana