Pengelompokkan Film Pada Platform Netflix Menggunakan Metode K-Means Clustering Sebagai Rekomendasi Film - Budihartanti, Cahyani; Ifaru, Cyrill Ilario; Zahra, Alfia; Aenuddin, Muhammad Hardithya; Setiawan, Eri Nur; Maulana, Cahyadi Anugrah; Pratama, Riky Putra (Journal of Information System Research (JOSH), 2024)
Netflix has become a major icon in technological developments in the world of entertainment. Advances in information technology allow us to make it easier to access thousands of film and TV show titles. Netflix is ​​a subscription service that makes it easier for users to watch movies and TV shows without ads on devices connected to the Internet. With the increasing number of film and TV show titles, it makes it difficult for viewers to decide which film to watch. The K-Means Clustering method is used to analyze data from various film preference groups on Netflix. The results of this analysis can help users choose film recommendations based on the rating and genre they want. In this way, it will be easier for users to make choices for watching movies and TV shows. The dataset taken came from Kaggle with 9957 data. The results of research using the K-means Clustering method can be concluded that there are 3 clusters, where Cluster 0 is short films to feature films with an average of 33% action genre and 20% comedy, cluster 1 is films - feature films to biographical or documentary films with an average of 37% action genre and 19% drama, and cluster 2 is series or short films with an average of 32% drama genre, 24% crime and 22% action.