THE IMPLEMENTATION OF K-NEAREST NEIGHBOR ALGORITHM FOR THE PREDICTION OF STUDENT GRADUATION TIME

  • Irwan Budiman Universitas Lambung Mangkurat
  • Dodon Turianto Nugrahadi Universitas Lambung Mangkurat
  • Radityo Adi Nugroho Universitas Lambung Mangkurat
Keywords: K-NN Algorithm; Data Mining; Prediction; Classification.

Abstract

A Collection of data on academic information system database Higher Education is often
not fully utilized, but from that data with data mining techniques can provide previously
unknown knowledge. Prediction timely graduation of students can be done using k-
Nearest Neighbor (k-NN) which is a method to perform the classification of objects based
on the training data that was located closest to the object. Prediction timely graduation
of students using the k-NN algorithm is done with the stages of data selection, data
transformation, data mining and interpretation. This study uses 154 data training and
data testing. The accuracy of predictions based on data testing taken by random is 90%.

Downloads

Download data is not yet available.

References

Banjarsari, Mutiara A. (2015) Pencarian k-Optimal pada Algoritma kNN untuk
Prediksi Kelulusan Tepat Waktu Mahasiswa Berdasarkan IP Sampai Dengan Semester 4. Jurnal KLIK, ISSN:2406-7857, Vol 2, No 2
Delavari, N. (2008) Data mining Application in Higher Learning Institutions.
Faculty of Information Technology, Multimedia University : Malaysia.
Fayyad, U. M. (1996) Advances In Knowledge Discovery and Data mining. Camberidge. MA:The MIT Press.
Han, J.,& Kamber, M. (2006) Data mining Concept and Tehniques. San Fransisco: Morgan Kauffman.
Larose, D. T. (2005) Discovering Knowledge in Data. New Jersey : John Willey & Sons, Inc.
Wu X, Kumar V. (2009) The Top Ten Algorithms in Data mining. New York: CRC Press.
Published
2017-11-20