Analisis Kinerja Struktur Data Kd-Tree Pada Metode K-Nearest Neighbors

Authors

  • Yuan Lukito Program Studi Teknik Informatika, Fakultas Teknologi Informasi

DOI:

https://doi.org/10.30606/rjocs.v2i2.866

Keywords:

KD-Tree, K-Nearest Neighbors, Array

Abstract

K-Nearest Neighbors is a commonly used classification technique that can be categorized into instance-based classification method. The performance of KNN is mostlydetermined by the size of the training data. This research compared and analyzed KD-Tree andArray data structures on KNN implementation. Dataset used in this research has largemultidimensional features. From the experiment conducted we can conclude that KD-Tree datastructure has better and relatively stable performance compared to Array data structure.

References

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Web: http://java-ml.sourceforge.net/ap i/0.1.7/net/sf/javaml/co re/kdtree/KDTree .html

Lukito, Y., Chrismanto, A., (2015). Perbandingan Metode-Metode Klasifikasi Untuk Indoor

Positioning System. Jurnal Teknik Informatika dan Sistem Informasi. 1 (2): 123-131.

Mitchel, T.M. (1997) Machine Learning. Portland: McGraw-Hill.

Skiena, S.S. (2008) The Algorithm Design Manual (2nd Edition). London: Springer-Verlag.

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Published

2016-08-06