Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First

The paper is tell about how to measure the potential of students' academic skills by using the parameter values and the area by using clustering analysis comparing two algorithms, algorithm K-Means and Farthest First algorithm. The data used in this paper is the student data of private universities in Indonesia. Tools that used in this study is Weka data mining application. From the results observed, found that the origin of high school affect the values obtained during the lectures and the more the number of clusters desired, the more also the time required to perform the data clustering.