Movies Selection Recommendation using Hybrid Filtering and K-Nearest Neighbor

Abstract

Movies are getting more popular as time goes by. This improvement in popularity is followed by the improvement of society’s interest in watching movies. However, with this improvement in movie’s world, the number of movies continues to increase over time, so the people need information that could help in determining the movie to watch. The result of trial scenario that has been done shows that K-Nearest Neighbor algorithm have successfully applied to the application. According to users’s satisfaction test, it’s known that users’s satisfaction with the recommender system that has been built reached 82.6%. The result of reliability test that using Cronbach Alpha reached 0.7, so it’s concluded that the questionnaire is reliable. Validation test that has been done also showed that the questionnaire is valid.