The PLATO stellar analysis pipeline will provide photometric rotation and activity measurements for as many observed stars as possible. This poster is aimed at presenting the scientific abilities of the rotation and activity analysis module that will be implemented in the PLATO pipeline. The strategy combines Fourier analysis, autocorrelation of time series and machine learning classifiers to extract both stellar surface rotation and long-term modulations related to stellar magnetic activity that might be detectable in the light curve. The ROOSTER random forest methodology guarantees the completeness and the robustness of this automated analysis. To showcase the potential of the designed framework, the algorithms were applied on a set of simulated light curves including spot evolution and migration, magnetic activity cycles, convective granulation, PLATO systematics, and random camera noise. From that analysis, we demonstrate that PLATO should already be able to provide rotation periods and photometric activity indices for a large sample after the first six months of observation, and that the quality of these measurements will be refined as longer time series are acquired.