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There are many free applications that need users to sign up before they can use the applications nowadays. It is difficult to choose a suitable password for your account. If the password is too complicated, then it is hard to remember it. However, it is easy to be intruded by other users if we use a very simple password. Therefore, biometric-based approach is one of the solutions to solve the issue. The biometric-based approach includes keystroke dynamics on keyboard, mice, or mobile devices, gait analysis and many more. The approach can integrate with any appropriate machine learning algorithm to learn a user typing behavior for authentication system. Preprocessing phase is one the important role to increase the performance of the algorithm. In this paper, we have proposed ensemble-by-session (EBS) method which to operate the preprocessing phase before the training phase. EBS distributes the dataset into multiple sub-datasets based on the session. In other words, we split the dataset into session by session instead of assemble them all into one dataset. If a session is considered as one day, then the sub-dataset has all the information on the particular day. Each sub- dataset will have different information for different day. The sub-datasets are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm after the preprocessing phase.