Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm
| Main Authors: | Imron, Mohammad, Kusumah, Satia Angga |
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| Format: | Article info application/pdf eArticle |
| Bahasa: | eng |
| Terbitan: |
Bright Publisher
, 2018
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| Subjects: | |
| Online Access: |
http://ijiis.org/index.php/IJIIS/article/view/17 http://ijiis.org/index.php/IJIIS/article/view/17/16 |
Daftar Isi:
- The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.