Aplikasi Pendukung Diagnosis COVID-19 Yang Menganalisis Hasil X-Ray Paru-Paru Dengan Model EfficientNet - Ananta Kusuma Pangkasidhi; Henry Novianus Palit; Alvin Nathaniel Tjondrowiguno (Jurnal Infra, 2021)
In December 2019, a new type of corona virus was detected that had symptoms of pneumonia in the seafood market in Wuhan City, Hubei Province, China. The virus then spread throughout the world, which in March 2020, WHO announced the status of the epidemic as a pandemic. WHO finally named this virus as COVID-19. COVID-19 has infected more than 105 million people worldwide, and deaths that have reached more than 2.3 million worldwide. In Indonesia alone there have been more than 1 million cases of COVID-19 and more than 30 thousand deaths in February 2021 . Based on number of cases, patient must be handled responsively. One of the supporting diagnosis for COVID-19 is Chest X-Ray. Chest X-Ray becomes one of the mandatory steps for patients to confirm and determine the treatment(s) to medicate the patients appropriately.In this study using the Deep Learning EfficientNet architecture to classify people affected by COVID-19, pneumonia, and normal from Chest X-Ray. The test results are measured by Accuracy, F1-Score, recall, precision, and specificity. With this research it is expected to be able to detect as quickly as possible so that it reduces the spread of COVID-19 and is more cost-effective because Chest X-Ray is cheaper, faster, and less radiation than CT-Scan. The result is that the accuracy in this study reaches 96 percent, and the F1-Score, Recall, Precision, Specificity is above 95 percent.