OPTIMIZING K-MEASN ALGORITHM USING PARTICLE SWARM OPTIMIZATION TO GROUP STUDENT LEARNING PROCESSES

In the implementation of learning, there are several factors that affect the student learning process, including internal factors, external factors, and learning approach factors. Internal factors (factors within students), for example: the physical and spiritual condition of the student. Namely: physiological aspects (body, eyes and ears) and psychological aspects (student intelligence, student attitudes, student talents, student interests and student motivation). External factors (factors from outside students), for example: environmental conditions around students. Namely: social environment (family, teachers, community, friends) and non-social environment (home, school, equipment, nature). While the student learning approach factors, for example: The learning approach factor, namely the type of student effort which includes the strategies and methods used by students to carry out learning activities of subject matter, which consists of a high approach, medium approach and low approach. So the first focus of this research is to do student clustering based on their learning process using 11 parameters. Second, using the PSO algorithm to get maximum clustering results. The research data were obtained from vocational secondary education institutions in the city of Pasuruan. Where the data is data obtained from the results of school reports and questionnaires as much as 350 student data. Data attributes include environmental features, social features, and related school features to group student data for learning data processing. From the classification results using the PSO method, there are 0.97140754 silhouette values that are obtained because the distance between the data is very close. From these results indicate that the PSO method is able to improve the performance of the k-means clustering method in the classification process of student learning interest.