The era of information and communication technology makes the information available on the internet growing rapidly. Recommender Systems are one of the technologies that are widely used to filter information to handle the huge of information. One of the developing information is film. The increasing number of films released every year has led to the development of applications that offer movie streaming services such as Netflix, Viu, Disney Hotstar, etc. Therefore, movie recommender systems technology is needed to facilitate and provide a good experience when users use these services. The purpose of this study is to conduct a Systematic Literature Review (SLR) to analyze methods against the algorithm developed in building a movie recommender system. SLR method consists of three stages, namely, planning, conducting, and reporting processes. Studies published from 2010 to 2020 were considered. There were 21 main studies in which the collaborative filtering method was used in 16 studies, knowledge-based filtering was used in 2 studies, and hybrid filtering method was used in 3 studies. The results of the SLR process can be concluded that there are advantages and disadvantages to each method developed in building the movie recommender system. However, the model-based collaborative filtering method is one method that can minimize cold start, data sparsity, and scalability problems.