<![CDATA[This applied research seeks to examine the dynamics of Covid-19 transmission in South Sulawesi Province. Additionally, this study will also forecast the future spread of Covid-19. This research comprises two phases: the investigation of Covid-19 transmission utilizing the SEIRS mathematical model, incorporating Vaccination and PPKM (Enforcement of Community Activity Restrictions), and the prediction of Covid-19 spread through machine learning techniques. The utilized data is secondary data sourced from the South Sulawesi Provincial Health Office. This study developed a machine learning model utilizing a random forest regressor algorithm due to its proficiency in identifying nonlinear data patterns. This model effectively accounts for the variability of the dataset (target variable) with a R2 score of 95%. The evaluation findings of the random forest regressor model indicated satisfactory performance, with a mean absolute error (MAE) of 29.57 and a root mean square error (RMSE) of 54.42 during training, and an MAE of 58.55 and an RMSE of 98.67 during testing. Given the significant variability in Covid-19 data and the prevalence of zeros in the dataset, the MAE and RMSE values for both training and testing are deemed acceptable. This model is designed to forecast daily Covid-19 instances in the future. This work not only employs machine learning but also analyzes the dissemination of Covid-19 through the SEIRS mathematical model, incorporating vaccination and PPKM factors. The SEIRS model analysis indicates that the disease-free equilibrium point is stable when R01 and unstable when R01. The fundamental reproduction rate derived from the vaccine's efficacy is v=65.3%, and the compliance rate with PPKM is ?=1%. Consequently, R0=1.2407288, indicating that Covid-19 will propagate in South Sulawesi Province. For ?=11%, R0=1.0167230. This indicates that Covid-19 will stabilize, and with ?=69%, R0=0.2338689, suggesting that Covid-19 will vanish from South Sulawesi Province.?]]>
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