Recommendation System for Clustering to Allocate Classes for New Students Using The K-Means Method

Abstract

SMAN 1 Durenan has a plan to organize the allocation of classes for new students using a system to achieve practical and efficient student grouping. The reason for implementing this class allocation system is SMAN 1 Durenan aims to create a new system to process student data for class allocation according to specific needs. This research involves the development of a Recommendation System for Clustering to Allocate Classes for New Students using the K-Means method. The system processes data of newly enrolled students at SMAN 1 Durenan based on specific attributes. The results of this student data processing serve as considerations and references for SMAN 1 Durenan to perform class allocation as needed. The analysis in this research utilizes the K-Means method to obtain data clusters that maximize the similarity of characteristics within each group and maximize the differences between the collections created. The developed recommendation system website provides information about the student data clustering results from the K-Means process at SMAN 1 Durenan.