User-Based Collaborative Filtering Using Agglomerative Clustering on Recommender System Malim Muhammad {malim.muhammad@gmail.com} Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Muhammadiyah Purwokerto Abstract. Content-based, collaborative filtering, demographic, knowledge-based, and hybrid recommender systems are the five categories of recommendation systems. User- based collaborative filtering and item-based collaborative filtering are the two types of collaborative filtering. However, the user-based approaches can be claimed to represent the user; researchers will employ them here. This method is more concerned with the user's likeness, or similarity than with the user's evaluated item. The accuracy of user-based collaborative filtering approaches employing agglomerative Clustering with similarity computations, i.e., cosine similarity, is improved in this study. MovieLens (https://grouplens.org/datasets/movielens/) provided the researchers with the data they needed. Between January 9, 1995, and October 16, 2016, a total of 100004 ratings for 9,125 films were collected from 671 individuals. At least 20 movies have been rated by each user. Each rating has a value of 1 to 5. The data utilized for testing is five value data from each user. In other words, 3,355 data points were tested in total. Using the single linkage clustering approach to cluster films in the use-based method has been shown to improve the accuracy of results that differ significantly between scenarios one and two, namely 3,409 and 3.26. MAE and RMSE are the accuracy gauges utilized in the analysis, and the smaller the value (closer to zero), the better the program results. The findings of two trials (2 Scenarios) revealed significant differences between scenario 1 and scenario 2, namely 3,409 and 3.26. This is because in scenarios 1 and 2, only neighbors with similarity values greater than zero are utilized to find predictions, regardless of whether the neighbor has scored the film to be forecasted or not. In scenario 1, however, the results produced by adding the single linkage clustering approach to the user-based method as mentioned above are not as good. As the value obtained grows larger, the system's level of accuracy decreases. However, the results achieved in scenario 2 are smaller, but the differences are not significant. Keywords: Recommended System, User-Based Collaborative Filtering, Agglomerative Clustering 1 Introduction Advances in technology have made the digital search easier. Over time various sites that use search engines, be it selling sites or other sites, also use a recommendation system. The recommendation system can be used in various areas, such as movies, news, music, books, and others. According to Casey (2014), the recommendation system utilizes the history of user behavior such as articles that have been read, products that have been assessed or purchased, ISTED 2021, July 17-18, Purwokerto, Indonesia Copyright © 2021 EAI DOI 10.4108/eai.17-7-2021.2312410