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The spread of the Corona Disease Virus (COVID-19) in Indonesia occurred quite quickly. It’s proven by the time interval of a month since its inception in Indonesia, as many as 1790 cases have occurred. If the number of cases that occur in the next day can be predicted, the government can do prevention. This articel will discuss the analysis of COVID-19’s data in Indonesia to predict the number of cases the following day. This analysis uses time series estimation with the type of data count. One of the famous approaches is using Observation Driven Model (ODM). This class is also divided into two parts, namely parametric and non-parametric. The non-parametric model used is Integer-valued Autoregressive (INAR), while the parametric model used is Autoregressive Conditional Poisson (ACP). Between the two models, the best model that has been built will be chosen. The selection is based on the value of the Akaike Information Criteria (AIC). The results show that the ACP model(1,1) better than INAR(4) for the prediction of data COVID-19 in Indonesia. The ACP(1,1) model produced a prediction of COVID-19 positive cases in Indonesia on July 1st, 2020 as many as 1160 cases. The cumulative number of cases that have occurred is 56385. This prediction has a Mean Absoulte Error (MAE) of 169.3.

Prediction; COVID-19; INAR(p); ACP(p,q)