Testing the K-Means Clustering Algorithm in Processing Student Assignment Grades Using the RapidMiner Application

Pandi Barita Nauli Simangunsong (Politeknik LP3I Medan)
Mamed Rofendi Manalu (Universitas Imelda Medan)

"Journal of Data Science
Vol. 1 No. 02 (2023): Journal Of Data Science, September 2023

In the current era of computers and the internet, educational data analysis has become very important to optimize the teaching and learning process. This study focuses on the use of the K-Means clustering algorithm used by the RapidMiner application to group assignment grades given to students during one academic semester. The goal is to discover patterns of achievement and areas that require intervention. The results show that the algorithm is very effective in identifying groups of students based on their performance and dividing them into middle, high, and those who need help. In short, the use of K-Means Clustering with RapidMiner offers a useful analytical approach for education. This allows for a more customized learning approach that is based on analysis of student achievement.