CLUSTERING DATA NILAI UJIAN AKHIR SEMESTER MENGGUNAKAN ALGORITMA DATA MINING K-MEANS

Abstract

Analyzing student performance in education is an active and fairly new area of educational research. Mapping student performance is useful for both teachers and students. However, the factors that affect student performance need to be identified first to build the  initial predictive model. The clustering of UAS scores acts as an indicator of whether the spread of knowledge from teachers to students is evenly distributed or not. This is very relevant to the state of the covid pandemic that is happening in Indonesia and the world, we can know which clusters of students are truly self-study or not. K-Means can be used to map students' academic abilities, in order to optimize student learning and understanding.