In 2018, the Ministry of Industry (Kemenperin) stated that the food and beverage sector managed to contribute 6.34% of the national gross domestic product (GDP). Nowadays, the culinary information can be easily found, both in print and online media. A lot of available information sometimes makes a person to have overload information, so they have difficulty in choosing the restaurant based on their preferences. In order to help the consumers in choosing the restaurant, we need a system that can provide some recommendations. This study aims to implement an item-based collaborative filtering method using a cosine algorithm that is adjusted according to the system on restaurant. The test was conducted with 40 samples from UIN Syarif Hidayatullah Jakarta by using purposive sampling. Accuracy testing uses precision and the determination of the error value uses MAE. Analysis of the results of the study used 3 scenarios, which are 5, 20 and 40 users. The third scenario produces the best precision and MAE values, amounting to 0.732 and 0.623. It can be concluded that the item-based method has the best accuracy value and the least error value when the number of users continues to grow.

Keywords : Recommender System, Item-based Collaborative Filtering,
Adjusted Cosine Similarity, Restaurant, Similarity