Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm


  • Annisa Fadhillah Pulungan North Sumatera University
  • Muhammad Zarlis North Sumatera University
  • Saib Suwilo North Sumatera University




Classification, K-Nearest Neighbor


Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.

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Alamri, S.S.A., Bin-Sama, A.S.A., & Bin-Habtoor, A.S.Y. (2016). Satellite Image Classification by Using Distance Metric. International Journal of Computer Science And Information Security.
<a href=",5">Google Scholar</a>

Hu, L.-Y., Huang, M.-W., Ke, S. –W., & Tsai C.-F. (2016). The distance function effect on K-Nearest Neighbor classification for medical datasets. SpringerPlus.
<a href=",5">Google Scholar</a>

Kaur, D. 2014. A comparative study of various distance measure for software fault prediction. International Journal of Computer Trends and Technology (IJCTT).
<a href=",5">Google Scholar</a>

Moghtadaiee, V. &Dempster, A. 2015. Vector distance measure comparison in indoor location fingerprinting. International Global Navigation Satellite Systems Society (IGNSS Symposium).
<a href=",5">Google Scholar</a>

Mulak&Talhar, N. 2015. Analysis of Distance Measurs Using K-Nearest Neighbor Algorithm on KDD Dataset. International Journal of Science and Research (IJSR).
<a href=",5&scioq=Vector+distance+measure+comparison+in+indoor+location+fingerprinting">Google Scholar</a>

Okfalisa et al. 2017. Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Data Classification. International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

Vashistha, R., & Nagar, S.2017. An intelligent system for clustering using hybridization of distance function in learning vector quantization algorithm. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-7.
<a href="">Google Scholar</a>

Viriyavisuthisakul, S.,et al. 2015. A comparison of similarity measures for online social media Thai text classification. 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1-6.
<a href=",5&scioq=Vector+distance+measure+comparison+in+indoor+location+fingerprinting">Google Scholar</a>

Wurdianarto, S.R., Novianto,S. &Rosyidah, U. 2014. Perbandingan euclidean distance dengan canberra distance pada face recognition. Techni.COM13(1): 31-37.
<a href=",5&scioq=Vector+distance+measure+comparison+in+indoor+location+fingerprinting">Google Scholar</a>


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How to Cite

Pulungan, A. F., Zarlis, M., & Suwilo, S. (2019). Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 4(1), 74-77.

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