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All Journal Jurnal Dimensi Pratama Teknik Sipil Livestock and Animal Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Riset Manajemen dan Bisnis (JRMB) Fakultas Ekonomi UNIAT JURNAL ATOMIK Jurnal ULTIMATICS JMTS: Jurnal Mitra Teknik Sipil Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Management - Small and Medium Enterprises (SME's) MEDIA BISNIS Jurnal Ilmiah Wahana Pendidikan COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat E-Jurnal Akuntansi TSM Journal of International Multidisciplinary Research The Indonesian Journal of General Medicine Harmonia : Journal of Music and Arts Jurnal Sistem Transportasi & Logistik
Jonathan Jonathan
Mahasiswa Program Studi Teknik Sipil Universitas Kristen Petra
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Journal : Jurnal ULTIMATICS

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Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3652

Abstract

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.
Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3652

Abstract

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.
Co-Authors A. Jaelani, A. Ahmad Sidik Alexandra, Jessica Andi Andi Aniek Prihatiningsih Anton Anton Aradena, Athallah Aqil Ghani Halim Darma, Wika Dwi Hairani, Rita Hartadi, Haris Jaya, Vionna Liang, Suwarno Liucius, Yenny Untari M. I. Zakir, M. I. Mahrita, Ani MUNGNIYATI, MUNGNIYATI Munthe, Yesyurun Nera Marinda Machdar Paretta, Shelomita Purbanuara Parlindungan Sitorus Puspita, Sesaria Mardhiani Rachma Rahfi, Muhamad Sauqi Rasidah Rasidah, Rasidah Ridhayati Farid Ronald Ronald Ruga, Ritbey Sadihatmanta, Benedicta Gisella Salim, Hartini Samantha, Maria Siahaan, Magda Sianturi, Mahmud Sihotang, Rahel Noprianti Sintalegawa, Jasmine Ambun Siti Rohani Suryadibrata, Alethea T. Rostini, T. Tedjo, Tony Tetty, Daniel Edward Timotius, Frenky Albert Tony Jack Tan Ding Widjaja, Moeljono Wijaya, Hendy Yana Tatiana, Yana Yuliana Yuliana