Recommendation System Using the K-Nearest Neighbor Approach: A Case Study of Dual Camera Quality as a Smartphone Selection Criterion

Abstract

Many smartphones today need to be more precise about choosing one that suits the user's needs. In fact, smartphone sellers sometimes need help recommending smartphones that suit buyers' needs. Generally, buyers search for smartphone specifications with keywords they desire, but the results appear different from what they expected. Users need the main specifications, such as Random Access Memory (RAM) and Read Only Memory (ROM) capacity, battery, and high camera quality. This research aims to implement the K-Nearest Neighbor (KNN) algorithm for recommendation smartphone selection based on the criteria mentioned. The data test results show that the combination of KNN with four criteria has good performance, as indicated by the accuracy, precision, recall, and f-measure values of 95%, 94%, 97%, and 95%, respectively.