IMPLEMENTATION OF K-MEANS CLUSTERING ANALYSIS TO DETERMINE BARRIERS TO ONLINE LEARNING CASE STUDY: SWASTA YAPENDAK TINJOWAN JUNIOR HIGH SCHOOL - Dinah Adillah; Nuriadi Manurung; Ari Dermawan (Jurnal Teknik Informatika (Jutif), 2022)
The grouping of online learning barriers to students during the covid-19 pandemic will result in clusters of students with the same characteristics in each cluster. The purpose of this study is to assist schools in determining online learning barriers for students during the covid-19 pandemic, so that with this clustering students with high levels of online learning barriers will get additional face-to-face hours.face-to-face learning so as to create an effective learning process. The method used in this study was a data mining technique, which uses the k-means clustering algorithm. This study uses the k-means clustering algorithm because this algorithm is more effective and efficient in processing large amounts of data, so this algorithm has a high enough accuracy for object size and the k-means algorithm is not affected by the order of objects. Testing the data using Microsoft Excel as a manual test and the PHP programming language and MySQL database. The results of this study were in the form of 2 clusters, C1 (low cluster) as many as 4 students who are hampered during online learning, and C2 (high cluster) as many as 16 students who are not hampered during online learning. The conclusion of this study was using of the k-means clustering algorithm can facilitate the grouping of online learning barriers for students at Swasta Yapendak Tinjowan Junior High School.