Design of a Movie Recommendation System Application Using a User-Based Collaborative Filtering Algorithm on Android

In today's digital era, the number of movies available on various streaming platforms continues to increase from year to year. This causes users difficulty in choosing a movie that suits their preferences. To overcome these problems, a recommendation system is needed that is able to provide personalized movie suggestions. This research aims to design an Android-based movie recommendation system application using the User-Based Collaborative Filtering algorithm. This algorithm works by analyzing the similarity of behavior and ratings between users to provide relevant movie recommendations. The system uses datasets from The Movie Database (TMDb) via API, and is designed with an intuitive and responsive user interface. The recommendation process is done by calculating the similarity between users, then predicting the rating of movies that have not been watched by users. The results show that the application is able to recommend movies accurately based on user preferences and interaction history of other users who have similar interests. With this system, it is expected that users can more easily find movies that suit their tastes, and contribute to the development of recommendation systems based on mobile platforms.