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All Journal Jurnal INKOM JURNAL NASIONAL TEKNIK ELEKTRO Journal of Physics and Its Applications Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika MULTINETICS Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Journal of Computer Engineering: Progress, Application and Technology Jurnal INFOTEL Kadaster: Journal of Land Information Technology
Umar Ali Ahmad
Universitas Telkom
Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Journal : JURNAL NASIONAL TEKNIK ELEKTRO

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Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network Maulana Akbar Dwijaya; Umar Ali Ahmad; Rudi Purwo Wijayanto; Ratna Astuti Nugrahaeni
JURNAL NASIONAL TEKNIK ELEKTRO Vol 11, No 1: March 2022
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (727.224 KB) | DOI: 10.25077/jnte.v11n1.984.2022

Abstract

COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.
Co-Authors Adam Aji Bhuwana Agung Nugroho Jati Ahmad Rif'a Al Muttaqi Alexander William Setiawan Putra Aliwarman Tarihoran Almuchlisin Almuchlisin Angga Febrian Angga Rusdinar Antaufany Puji Rahmadha Ariep Maaulana Yassar Asep Mulyana Ashri Dinimaharawati Asif Awaludin Bagas Wara Rachmat Ramadhan Bangkit Indarmawan Nugroho Budhi Irawan Burhanuddin Dirgantara Burhanuddin Dirgantara Burhanuddin Dirgantoro Danang Triantoro Murdiansyah Dedi Gunawan Degas Rinaldo Deni Rustandi Devi Pratami Dimas Chaidar Dyah Pramesthi Larasati Eryzebuan, Yon Sigit Fahmi Charish Mustofa Fairuz Azmi Fardilla Zardi Putri Fardilla Zardi Putri Fath Muhammad Isham Fauzi Sofyan Favian Dewanta Fussy Mentari Dirgantara Gian Nugraha Sugirman Hanu Handriadma I Putu Yuda Danan Jaya Ika Arum Puspita Ikbal Ramdani Istikmal Ivan Ramadhan Ramadhan Jati Satria Wicaksana Jati Satria Wicaksana Kevin Simangunsong Litasari Widyastuti, Litasari Mas'ud Adhi Saputra Masud Adhi Saputra Maulana Akbar Dwijaya Miftah Amirul Amin Muhamad Naufal Syaiful Bahri Muhammad Arham Irsyad Muhammad Emir Ghiffari Muhammad Ihsan Muhammad Nasrun Muhammad Raihan Mubaroq Muhammad Rifan Muhammad Solihin Muhammad Zain Imtiyaz Mulki Syahputra Muharram Nana Febriana Novianty, Astri Nurul Anggraini Nurul Ikhsan Perdana Erick Oktafianto Prasetyo Yuda Pangestu Prayitno Abadi R Rogers Dwiputra Setiady Rahadian Nugraha Randy Erfa Saputra Ratna Astuti Nugrahaeni Reza Ilmi Reza Rendian Septiawan Rifdo Shah Alam Robby Reza Rudi Purwo Wijayanto Septian Budi Asmara Taufik Ismail Tengku Ahmad Riza Utsman Al Aydarus Wildan Panji Tresna Yon Sigit Eryzebuan Yuliantho Mardiansyah Yuniar Pristyan Chandra Yusup Diva Pratama ZK Abdurahman Baizal