| Summary: | Human mobility in urban area related to how people moved from a city to another city, whether by walking or using vehicles to support their mobility. By processing data of human mobility, we can determine prediction of the next pattern of human mobility. Some methods for human mobility prediction have been proposed. One of them is predication using Markov. In this research, we conducted implementation of Markov algorithm to predict location of human mobility based on input data form individual mobility dataset (GeoLife) from GPS. This research carried out through five stages of research and conducted between December 2017 until June 2018. The conclusions drawn from this study are the values for parameters such as HMM n_components = 5, covariance_type = 'spherical', and decoder algorithm = 'viterbi' which produces a curation of 0.769 and RMSE 1,641 can be said to be hmm good enough in modeling data.
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