COMPARISON OF K-NEAREST NEIGHBORS AND NAÏVE BAYES CLASSIFIER ALGORITHMS IN SENTIMENT ANALYSIS OF USER REVIEWS FOR INTERMITTENT FASTING APPLICATIONS

Applications that focus on health, especially obesity prevention, are scattered in the Google Play Store, one of which is the "Intermittent Fasting" application, which, according to the developer, aims to help users maintain a healthy lifestyle and regulate eating habits. With the increasing number of similar health applications, this research focuses on sentiment analysis of user reviews of "Intermittent Fasting" to find out how users respond. The purpose of this research is to find the best algorithm to analyze sentiment on user reviews on the Google Play Store against the "Intermittent Fasting" application, as well as provide recommendations for new or old users and for application developers based on the results of processing review data. The data mining methodology used in this research is CRISP-DM, using a dataset collected on user reviews on the Google Play Store for five years (2019-2024), which is annotated with three sentiment labels (positive, negative, and neutral) based on user ratings, then modeling using two algorithms K-Nearest Neighbors (KNN) and Naïve Bayes Classifier (NBC). The contribution of this research is to test, evaluate, and compare the two algorithms (KNN and NBC) using two testing models (Split and K-Fold Cross Validation) and then provide recommendations for the best algorithm. The research concludes that the NBC algorithm is superior to KNN with an accuracy value of 80%, while the KNN algorithm has an accuracy value of only 71.43%. In addition, the K-Fold Cross Validation testing model is more optimal in improving the accuracy of the algorithm's performance than the Split model.