Performa Algoritma User K-Nearest Neighbors pada Sistem Rekomendasi di Tokopedia - Rama Dian Syah (Teknik Informatika Universitas Pamulang, 2020)
The biggest marketplace in Indonesia such as Tokopedia has data on e-commerce activities that always increase with time. Large data growth in Marketplace can cause problems for users. Buyers who have difficulty in finding the best product that suits their needs and sellers who have difficulty in promoting products that are often visited by buyers can be overcome. The recommendation system can overcome these problems by providing specific product recommendations to be promoted and offered to buyers. This research implements the Recommendation System using the Item Rating Prediction Method by applying the User K-Nearest Neighbors Algorithm. The Recommendation System provides recommendations based on ratings on products given by the buyer. Algorithm performance in Recommendation System is measured by the parameters of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Mean Absolute Error (NMAE). The performance values obtained are RMSE = 0.713, MAE = 0.488 and NMAE = 0.122. Perfomance values below 1 proves that the User K-Nearest Neighbors Algorithm is suitable as a rating prediction model on recommendation system.