Athletic Scholarships Have a Huge Impact on Black Student Graduation Rates

School as one of the processes for implementing formal education is required to carry out the learning process optimally to produce quality students. Regarding the research process carried out to predict the graduation rate of SMA Nurul Falah students by using the decision tree method. The data used in this study are student data using the criteria for student names, majors, average report cards from semester one (I), two (II), three (III), four (IV), five (V), and the average value of the National Standard School Examination (USBN). The data is then managed using Rapidminer 5.3 software to make it easier to predict student graduation rates. The application of data mining is used to predict the graduation rate by using the decision tree method and C4.5 algorithm as a supporter as well as to find out information on the graduation rate of Nurul Falah High School students. This study aims to predict student graduation rates in order to get useful information and the school can make policies in the coming year.

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The Persisting Racial Gap in College Student Graduation Rates

The Journal of Blacks in Higher Education ◽

10.2307/4133624 ◽

2004 ◽

pp. 77

Keyword(s):

College Student ◽

Graduation Rates ◽

Racial Gap ◽

Student Graduation

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The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate

Indonesian Journal of Artificial Intelligence and Data Mining ◽

10.24014/ijaidm.v1i1.5654 ◽

2018 ◽

Vol 1 (1) ◽

pp. 47

Author(s):

Imam Ahmad ◽

Heni Sulistiani ◽

Hendrik Saputra

Keyword(s):

Graduation Rates ◽

Graduation Rate ◽

Nearest Neighbor ◽

Training Data ◽

Pass Rate ◽

K Nearest Neighbor ◽

First Semester ◽

Long Time ◽

Student Graduation ◽

Graduating Students

The absence of prediction system that can provide prediction analysis on the graduation rate of students becomes the reason for the research on the prediction of the level of graduation rate of students. Determining predictions of graduation rates of students in large numbers is not possible to do manually because it takes a long time. For that we need an algorithm that can categorize predictions of students' graduation rates in computing. The Fuzzy Method and KNN or K-Nearest Neighbor Methods are selected as the algorithm for the prediction process. In this study using 10 criteria as a material to predict students' graduation rate consisting of: NPM, Student Name, Semester 1 achievement index, Semester 2 achievement index, Semester 3 achievement index, Semester 4 achievement index, SPMB, origin SMA, Gender , and Study Period. Fuzzyfication process aims to change the value of the first semester achievement index until the fourth semester achievement index into three sets of fuzzy values are satisfactory, very satisfying, and cum laude. Make predictions to improve the quality of students and implement KNN method into prediction, where there are some attributes that have preprocess data so that obtained a value, and the value is compared with training data, so as to produce predictions of graduating students will be on time and graduating students will be late. This study produces a prediction of student pass rate and accuracy.

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