Implementation of K-Means Clustering for Analysis Students English Proficiency

Abstract

English is one of the international languages. Language is a communication tool that is carried out orally or in writing. 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 the existence of data mining technology, an analysis of students' English skills can be carried out. This analysis was carried out by grouping students according to ability scores in these empathy skills. In conducting this research, the K-Means clustering method was used to classify students' English skills. With the K-Means clustering technique, it is hoped that the teacher can adjust the learning model according to the students' abilities. Based on the grouping results, the grouping with 3 clusters is the most optimal grouping result with the smallest Davies Bouldin Index (DBI) value, namely 0.365. The application of the K-Means method in grouping student data based on English proficiency scores can produce 3 groups of students who are smart, moderate, and moderate.