Performa Klasifikasi Berbasis Jarak untuk Deteksi Covid-19 Varian Delta dan Omicron Menggunakan Citra CT-Scan Paru-Paru

Abstract

During the Covid-19 pandemic, there were two popular Covid-19 variants, namely Delta and Omicron. A non-laboratory approach is needed to detect the Delta and Omicron variants of Covid-19 to prevent a high risk of exposure to these two variants. This study proposes the detection of COVID-19 variants of Delta and Omicron using computerized tomography scan (CT-scan) images of the lungs using distance-based classification. There are 5 distance-based classification methods used to determine the best performance for the Delta and Omicron variant Covid-19 classification. Performance is measured based on the comparison of accuracy, precision and recall of each distance method. The distance method used in this study is Euclidean, Manhattan, Minkowski, Chebyshev, and Canberra. The dataset used was downloaded from the Kaggle database. There are 440 total CT-scan images of the lungs which are divided into 220 Covid-19 images of the Delta and Omicron variants and 220 non-Covid-19 images as training data. Meanwhile, there are test data of 140 Covid-19 images for the Delta and Omicron variants and 140 non-Covid-19 images. Based on the comparison of the performance of distance-based classification, it is concluded that the Manhattan Distance has the best performance compared to the other 4 distance methods. Manhattan distance has an accuracy of 58.57%, precision of 56.52%, and recall with a value of 74.28%. Meanwhile, the lowest accuracy value is owned by the Eucliean Distance of 48.21%. Then, the Minkowski distance has the lowest precision and recall with values ​​of 48.41% and 54.28%.