COMPARISON OF PREDICTION ANALYSIS OF GOFOOD SERVICE USERS USING THE KNN & NAIVE BAYES ALGORITHM WITH RAPIDMINER SOFTWARE
Abstract
GoFood is a service provider that has a very important role in human life, especially in this growing era. Currently, many service providers are competing to meet the needs of users, including GoFood. However, not all service providers can meet and know the needs needed by users, because they focus on the services offered and only the quality of services provided. Therefore, survey analysis is needed to obtain customer satisfaction data that will be used to satisfy GoFood service users. The classification method uses the KNN and Naive Bayes algorithms, which are good algorithms for testing 1,000 records of GoFood user data that have been obtained previously. The test results using Cross Validation and T-Test show that the KNN algorithm is the best algorithm with 98.80% Accuracy and 100% Recall, while Naive Bayes obtains 94.10% Accuracy and 94.43% Recall.
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