K-Means cluster optimization for potentiality student grouping using elbow method Available to Purchase

The grouping of potential students conducts to determine the student's interest and increase the student's academic performance. The K-Means algorithm could do collection or clusterization. This study aims to implement one of the Machine Learning algorithms, K-Means, to classify the potential of interest grouping of Informatics Engineering student's batch 2019 at the Universitas Muhammadiyah Purwokerto. The process of categorization was based on average course values, which are a part of student specializations, namely 1) Intelligent Systems (IS), 2) Software Engineering (SE), 3) Computer Networks (CN), and 4) Multimedia (MM), as well as student's GPA data (semester 1 to semester 4). Moreover, this research involves the Elbow method for determining the number of optimal clusters and Sum of Squared Errors (SSE) as a cluster validation technique. From the Elbow process, Within Cluster Sum of Squares (WCSS) significantly decreases when K is significantly upwards from 2 to 3, and the SSE maximum rate of change is 71.29 %. Therefore, the optimal cluster is 3. With K-Means clustering results, the majority of the students (62 or 41.05 %) are assigned to the Intelligent System group, the second majority (59 or 39.07 %) to the Multimedia group. At the same time, a cluster of Computer Networks was the group with the fewest members.