In the business world, the application of technology is becoming common, including in the process of buying or ordering food products which can now be done through a mobile application. Makecents Coffee is a startup in the city of Medan that provides solutions for ordering food and drinks at Android-based restaurants using the QR Code ordering system. To make it easier for buyers to place orders, an automatic recommendation system is needed. One method that can be used to develop an ordering application with a recommendation system is a collaborative filtering algorithm. In this study, a collaborative filtering algorithm was used to work by storing and processing data provided by buyers, such as ratings or comments on the food menu ordered. Using buyer data provides results for users in placing orders because they use an application that has them, as well as making it easier to choose a menu to order because of a recommendation system. The level of accuracy of the prediction of the collaborative filtering algorithm itself has been tested using the MAE and RMSE tests. Where the MAE test obtained a value of 0.67 points, while the RMSE test obtained a value of 0.58 points. The two test results were fairly good when compared to the range of points which only ranged from 1 to 5 points. The results of the recommendations can be implemented in applications designed to increase sales and make it easier to place orders that have been recommended to users.

Implementation of Collaborative Filtering Algorithms in Mobile-Based Food Menu Ordering and Recommendation Systems