Comparison of K-Means and K-Medoids Algorithms in Students English Skill Clasterization
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Abstract
Students who have the ability to speak English well can communicate their ideas and ideas in the school environment or with foreigners. English proficiency is not only the ability to speak, but also the ability to understand and produce spoken or written texts which are realized in the four language skills namely listening, speaking, reading and writing. With data mining technology, it is possible to analyze the value of students' English skills. This analysis was carried out by grouping students according to their ability scores in the four skills. In conducting this research, a comparison of the K-Means and K-Medoids clustering methods was used to classify students' English abilities. With the clustering technique, it is hoped that the teacher can adjust the learning model according to the abilities of the students. The purpose of this study is to analyze and process data by comparing the K-Means and K-Medoids methods in clustering English skills scores. Based on the research that has been done, when compared to the K-Means method with K-Medoids, the K-Medoids method is more optimal in terms of the lowest Davies Boldin Index (DBI) value of 0.287 at k=3.
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