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All Journal Bulletin of Electrical Engineering and Informatics J I M P - Jurnal Informatika Merdeka Pasuruan
Brian Aditya Hermansyah
Universitas Muhammadiyah Surakarta
Computer Science & IT Electrical & Electronics Engineering

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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.
Transfer learning for detecting COVID-19 on x-ray using deep residual network Helmi Imaduddin; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4334

Abstract

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.
Klasifikasi Kematian Akibat Gagal Jantung Menggunakan Algoritma Logistic Regression Berbasis Forward Selection Helmi Imaduddin; Brian Aditya Hermansyah; Muhammad Mutawadhi’ Alfajri
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 7, No 3 (2022): Desember
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51213/jimp.v7i3.565

Abstract

Gagal jantung adalah masalah kesehatan masyarakat utama yang beban penyakitnya meningkat seiring bertambahnya usia. Kondisi jantung dalam kasus ini menandakan bahwa jantung tidak mampu lagi untuk memompa darah secara optimal dan ketidakmampuan jantung dalam memenuhi kuota darah normal yang dibutuhkan oleh tubuh. Berdasarkan timbulnya gejala, gagal jantung dapat terjadi secara tiba-tiba atau lebih dikenal dengan gagal jantung akut, dan gagal jantung yang berkembang secara perlahan karena kondisi jantung yang melemah atau lebih dikenal dengan istilah gagal jantung kronis. Tujuan dari penelitian ini adalah mendapatkan model klasifikasi penyakit gagal jantung untuk membuat sistem penunjang keputusan sebagai deteksi dini penyakit gagal jantung. Setelah itu model yang sudah diperoleh akan dievaluasi untuk mengetahui performanya dengan akurasi, spesifisitas dan sensitivitas. Metode yang digunakan untuk melakukan klasifikasi menggunakan metode Support Vector Machine, Decision Tree, Logistic Regression dan Random Forest. Pengukuran performa klasifikasi menggunakan matrik akurasi, sensitivitas dan spesivisitas, hasil klasifikasi menunjukan bahwa algoritma logistic regression memiliki performa paling baik dengan memperoleh akurasi sebesar 90% dan spesivisitas 80%.
Co-Authors Azizah Fatmawati Fiddin Yusfida Ala Helmi Imaduddin Muhammad Mutawadhi’ Alfajri