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Research Paper | Statistics | Indonesia | Volume 7 Issue 1, January 2018 | Popularity: 6.8 / 10

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Implementation of Moderate Multiple Regressions (MMR) Methods in Decision Support System Providing Scholarships for Outstanding Student Education Aid at Udayana University

I W Santiyasa, I K Ari Mogi

Abstract: Scholarships are usually awarded to students who are considered achievers. Only so far the intended or assumed new achievement students are measured from the quantity of GPA Achievement Index (GPA) only, so many students are given to the students, but very few achievements obtained by students who can improve the university index. To achieve the achievements both regionally, nationally, and internationally, then in addition to high GPA, also required creativity and high innovation of students, especially those who get a scholarship. So it is necessary to re-analyze the patterns used as the basis for consideration in the scholarship application, so that the achievement targets achieved by the scholarship recipients both at the regional, national and international levels are increased, by incorporating the components / attributes of creativity and innovation in the components of the scholarship pattern. The method used in the preparation of this scholarship is Moderate Multiple Regression (MMR) as a modeling method in decision support system (Decision Support System), where all components of the achievement are GPA, Student Creativity Program (PKM), and innovations which has been done given weight in accordance with the level of achievement. Then do filtering to result of weighting from each component of achievement by method of MMR so that obtained by compilation variable from moderate multiple regression. After getting some MMR model, it is done by optimizing the pattern by calculating multivariate pla from every existing model. The model with the highest multivariate placement will be the optimal pattern in the provision of scholarships for student learning assistance

Keywords: Innovation, Scholarship, Decision Support System, Moderate Multiple Regression

Edition: Volume 7 Issue 1, January 2018

Pages: 705 - 708

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