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Covid-19 has spread to various countries in the world, including Indonesia. Surabaya becomes one of the big cities in Indonesia where the spread of Covid-19 is very fast, so the number of positive cases of Covid-19 is very large and more than 1000 people die because of this disease until November 2020. Prediction of the number of positive cases of Covid-19 can be used to regulate the availability of facilities in hospitals and make plans and policies to overcome this disease outbreak. Many prediction methods have been found, one of which is the Extreme Learning Machine (ELM). ELM has high training speed and accuracy. However, the performance of ELM depends on the number of neurons. When the number of neurons is not precisely determined, the accuracy of prediction becomes worst. Particle Swarm Optimization (PSO) is used to decide the number of neurons. For this reason, this paper proposes a prediction of the Covid-19 cases in the City of Surabaya using the hybrid of ELM and PSO (ELM-PSO). The experiments show that the comparative performance of the proposed methods with several activation functions in the prediction of the Covid-19 cases in the City of Surabaya. [ABSTRACT FROM AUTHOR]