Application of Data Mining to Measure Student Intelligence Level Using The K-Means Method

Intelligence is an individual's ability to understand, learn, and think to solve complex problems. Human intelligence includes intellectual, emotional, spiritual, and multiple intelligences. Asically, a person has different intelligence in many fields of discipline. This study focuses on measuring the level of intellectual intelligence of students at Takhassus Abu Hurairah Sukorejo School using the K-Means Clustering method implemented using the Rapidminer application. The K-Means method was chosen because the process is simple and relatively easy to apply to large datasets. Students are grouped into three clusters based on intelligence levels based on the closest distance to the centroid. The results show that the cluster with the closest centroid distance has the highest intelligence characteristics, while the cluster with the furthest centroid distance shows lower intelligence. With a Davies-Bouldin value of 0.599, it shows that the grouping applied is quite effective and optimal. The results of this study can be a reference for schools to determine the right learning strategy for each group of students that can increase the effectiveness of learning in schools.