A Comparison of The Predictive Ability between Logistic and Gompertz Model on COVID-19 Outbreak - Tita Haryanti; Brian Pamukti (IJAIT (International Journal of Applied Information Technology), 2021)
A predictive model can be learned using historical information. Thereafter, information about a running case is combined with a predictive model to estimate the case's remaining flow time. The predictive model is based on data from past events, which can be used to make predictions for current operating situations. For example, the case of coronavirus disease 2019 (COVID-19), which is currently infecting the whole world, including Indonesia, have influenced various aspects, ranging from the educational environment, business, economy, to the companies. Data scientists are urgently needed who can help organizations improve their operational processes. Therefore, this journal discusses the prediction of the peak number of COVID-19 cases in Indonesia, using two prediction models, logistic and Gompertz. The results obtained show that the Gompertz model has higher accuracy than the logistic model, with an accuracy of 99.85%. This journal's results are expected to help organizations estimate the time to rebuild themselves after being affected by COVID-19.