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Abstract

Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government). Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining) will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the  university may recommend actions to be taken to improve the student  achievement index and graduation rates.

Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithms

Article Details

How to Cite
Setiawan, A., & Suksmana L, K. (2015). Predicting Success Study Using Students GPA Category. The Asian Journal of Technology Management (AJTM), 8(1), 68–74. https://doi.org/10.12695/ajtm.2015.8.1.7

References

    Bramer, M. (2007). Principles of Data Mining. Springer, London.
    Giudici, P. (2003). Applied Data Mining Statistical Methods for Business and Industry, Wiley.
    Han, J. and Kamber, M. (2006). Data Mining Concepts and Techniques Second Edition. Morgan Kauffman, San Francisco.
    Pyle, D. (2003). Business Modeling and Data Mining. Morgan Kaufmann Publishers.
    Rud, O. P. (2000). Data Mining Cook Book, John Wiley and Sons Nov.
    Susanto, S., and Suryadi, D. (2010). Pengantar Data Mining, Menggali Pengetahuan dari Bongkahan Data, Andi Yogyakarta
    Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis, Graha Ilmu.