APPLICATION OF K-MEANS CLUSTERING METHOD TO CLUSTER STUDENTS’ ENGLISH SKILL JASON ENGLISH COURSE - Khairunnisa Khairunnisa; Juna Eska; Mustika Fitri Larasati Sibuea (Informatika, Universitas Jenderal Soedirman, 2022)
The role of students with high English skills and higher quality standards in an English course institution is one aspect that has a significant impact on the course institution's quality. To generate positive competition among students and to produce the best quality of students, student English skills should be clustered. The determination of students' English skills at the Jason English Course is currently done with the results of decisions that are off target, impenetrable, and inefficient because the calculations are still not computerized and depend on the tutor's personal feelings, causing it unachievable the initial goal of clustering these skills. The goal of this research is to make it easier for tutors to group students' English skills with targeted, transparent, and efficient decision results so that students can recognize their abilities and tutors can create appropriate learning methods so that students do not encounter learning obstacles. As a result, the course institution requires the appropriate technology to assist tutors in grouping students' abilities. The method used in this research was k-means clustering, which is a data mining technique. This research data is calculated and tested using Microsoft Excel, RapidMiner, Design Systems with PHP programming languages, and databases MySql. After calculating and testing the data, the accuracy obtained on the data sample reached 80%. As a result, using K-means clustering, Student English Skill Jason English Course can be clustered quickly, clearly, efficiently, and accurately.