Grouping Systems In Cooperative Oriented Problem Learning Model Using K-Means Clustering
The development of science and technology requires universities as formal educational institutions, to be able to produce qualified and competent graduates. Learning about tertiary institutions needs to be more innovative and creative in generating learning and responsive to the needs of the workforce. Obstacles that succeed lecturers in teaching Data Structure courses do not have learning models that approach students with abstract theories that are difficult for students to understand, to overcome these conflicts, it is necessary to have a learning model that can improve the quality of learning that can improve students learning by making groupings learning by applying Cooperative Oriented Problem learning models. However, in terms of grouping learning with the application of this method still requires a relatively long time which is done several times individual testing to find a suitable group, so that the grouping of learning is less than the maximum. The method used in this research is K-Means Clustering, from the software that was built to help the instructors in the data structure course in the process of grouping student tutoring. The grouping method can be applied to build valid student tutoring grouping software.