Mapping Student's Performance Based on Data Mining Approach (A Case Study)

Abstract

Improving of student performance is one important focus of college management. Mapping of student's actual condition is the requirement that must be made before designing the performance improvement program. This paper focus on mapping students using K-mean Cluster algorithm to reveal the hidden pattern and classifying students based on their demographic (gender, origin, GPA, grade of certain courses), and avverage of course attending. About 300 student's data is covered. From the calculation using SPSS 16, it is found that there are four clusters formed based on six variables, namely: smart students (45.74), standart student (33.33%), and 20.92% belongs to low performance students.