CLASSIFICATION OF BASIC INFANT IMMUNIZATION MOTIVATION USING BINARY LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINE (SVM)

Abstract

Culinary tourism has its own appeal for tourists visiting an area, but the tourists who come may not necessarily know the culinary offerings in that area, so a system is needed that can provide recommendations to tourists. A recommendation system is a system that can provide suggestions to its users regarding a particular item, and the suggestions given are used in various decision-making processes. The method used is Collaborative Filtering. The problem is how to apply the Collaborative Filtering method to recommend food with many influencing factors, resulting in a relevant recommendation. The recommendation process involves grouping users into a specific group through the clustering process using the K-Mean method, after which the software calculates the similarity between the user and the group members. The calculation of similarity between users and their group members uses the Pearson correlation coefficient formula. The determination of the recommendation results provided uses a ranking system with the highest recommendation values. The data used consists of 18 food data, 100 training data, and 10 testing data. The results of the relevance percentage test reached 80%.