PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA

  • Tyas Setiyorini STMIK Nusa Mandiri
  • Rizky Tri Asmono Teknik Informatika STMIK Swadharma
Keywords: Student Performance, K-Nearest Neighbor, Information Gain

Abstract

Education is a very important problem in the development of a country. One way to reach the level of quality of education is to predict student academic performance. The method used is still using an ineffective way because evaluation is based solely on the educator's assessment of information on the progress of student learning. Information on the progress of student learning is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. K-Nearest Neighbor is an effective method for classifying student performance, but K-Nearest Neighbor has problems in terms of large vector dimensions. To solve this problem, the Information Gain feature selection method is needed to reduce vector dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with a value of k (1 to 10) on the student performance dataset using the K-Nearest Neighbor method showed the highest average accuracy of 74.068 whereas the K-Nearest Neighbor and Information Gain methods obtained the highest average accuracy of 76.553. From the results of these tests it can be concluded that Information Gain is able to reduce the vector dimensions, so that the application of K-Nearest Neighbor and Information Gain can improve the accuracy of student performance classification better than using the K-Nearest Neighbor method.

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Published
2019-06-26
How to Cite
Setiyorini, T., & Asmono, R. (2019). PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 5(1), 7-14. https://doi.org/10.33480/jitk.v5i1.613
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