Implementation of Data Mining to Measure Informatic Engineering Graduation Using KMeans Clustering Method
The Faculty of Computer Science at Lancang Kuning University is one of the favorite faculties among the faculties at Lancang Kuning University today. With a good graduation rate every year with the predicate graduation score is very satisfying. Each Study Program is obliged to monitor the progress of studies of its students. Then the study program also has the duty to pay attention to groups of students who have the potential to graduate on time and students who have the potential to experience a setback for the study period and even experience dropping out. To predict it can be done by using data mining techniques with the K-Means Clustering method. In this study, the use of Rapidminer software can be done to build a pattern of grouping the
results of student graduation rates using the K-Means Clustering method of data analysis grouping the graduation rate of 2016 academic year informatics students who have conducted lectures up to semester 6 (VI) using data Semester 2 to Semester 5 GPA and total credits taken previously. Prevention of failure is very important for management of study programs. New
knowledge gained in this study was used to assist study programs to better understand the situation of their students and to be able to anticipate drop-out students, to improve student achievement, to improve curriculum, improve the process of learning and teaching activities
and many other benefits that could be obtained from the results of mining the data.
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