标题：ANALYSIS AND IMPLEMENTATION OF ALGORITHM CLUSTERING AFFINITY PROPAGATION AND K-MEANS AT DATA STUDENT BASED ON GPA AND DURATION OF BACHELOR-THESIS COMPLETION
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：35
期号：1
页码：069-076
出版社：Journal of Theoretical and Applied
摘要：Effectiveness and accurate results from an algorithm has always been a basic reference for every step taken in the use and utilization of algorithm, which is expected to achieve optimal results both in quality and quantity. In order to realize the level of accuracy and effectiveness from the program, it would require an algorithm that can minimize error and faster in data processing rate compared with existing algorithm In this paper, we have compared two algorithms, namely Affinity Propagation and K-Means, at data student based on GPA and Duration of Bachelor-Thesis Completion. The results show that Affinity propagation gives the result of data cluster more accurate and effective than K-Means, it can be seen from the testing table which showing that the value of affinity propagation exemplar has not changed at all after five trials. While K-Means, gives values of its centroid are different after five trials. And at the data students itself, it show that there is a relationship between GPA and Duration of Bachelor-Thesis completion in Gunadarma University students, it can be seen from the results of data clustering, that is for student who have GPA above 3 to 4 have a tendency to finish their Bachelor-Thesis faster, which is less than 1 until 2 semesters. While other students who have GPA less than 3 have a longer time to finish their Bachelor-Thesis, within a period of 2 until more than 4 semesters.
关键词：Data Clustering; Affinity Propagation; K-Means; GPA; Thesis; Gunadarma University
