Python Model Predicts Covid-19 Cases since Omicron in Indonesia - Muhammad Furqan Rasyid (Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau, 2023)
The proposed work uses Support Vector Regression model to predict the new cases, recovered cases, and deaths cases of covid-19 every day during sub-variant omicron spread in Indonesia. We collected data from June 14, 2022, to August 12, 2022 (60 Days). This model was developed in Python 3.6.6 to get the predictive value of the issues mentioned above up to September 21, 2022. The proposed methodology uses a SVR model with the Radial Basis Function as the kernel and a 10% confidence interval for curve fitting. The data collected has been divided into 2 with a size of 40% test data and 60% training data. Mean Squared Error, Root Mean Squared Error, Regression score, and percentage accuracy calculated the model performance parameters. This model has an accuracy above 87% in predicting new cases and recovered patients and 68% in predicting daily death cases. The results show a Gaussian decrease in the number of cases, and it could take another 4 to 6 weeks for it to drop to the minimum level as the origin of the undiscovered omicron sub-variant. RBF (Radial Basis Function) very efficient and has higher accuracy than linear or polynomial regression as kernel of SVR.