DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.54-58

Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual

Comparison of distance measurement on k-nearest neighbour in textual data classification

*Wahyono Wahyono scopus  -  Department of Computer Science and Electronic, Universitas Gadjah Mada, Indonesia
I Nyoman Prayana Trisna  -  Master of Computer Science, Universitas Gadjah Mada, Indonesia
Sarah Lintang Sariwening  -  Master of Computer Science, Universitas Gadjah Mada, Indonesia
Muhammad Fajar  -  Master of Computer Science, Universitas Gadjah Mada, Indonesia
Danur Wijayanto  -  Master of Computer Science, Universitas Gadjah Mada, Indonesia
Received: 15 Jun 2019; Revised: 22 Oct 2019; Accepted: 5 Nov 2019; Published: 31 Jan 2020; Available online: 15 Nov 2019.
Fulltext |
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian and Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.
Keywords: KNN; tekstual data; distance measurement; Euclidean; Chebyshev; Manhattan; Minkowski
Funding: Universitas Gadjah Mada, Indonesia

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Statistics: 672 1049
Share:
Related articles
Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates Priority Road Restoration Classification Using Fuzzy C-Means: A Case Study In Samarinda City More related articles
Most cited articles
Perancangan dan Pengembangan Permainan “Super Sigi” Menggunakan Stencyl Sebagai Media Pengenalan Menyikat Gigi Perancangan Papan Informasi Digital Berbasis Web pada Raspberry pi Web Application of Lecturer Performance Assesment on Teaching Process: A Case Study at Internal Quality Assurance Agency of Institut Teknologi Padang Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Pengembangan Aplikasi Multimedia untuk Pembelajaran Satelit Astronomi NASA dengan Teknologi Augmented Reality Berbasis Android More cited articles
  1. R. Feldman and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press, 2007
  2. T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, 1967. doi: 10.1109/TIT.1967.1053964
  3. T. Bailey and A. K. Jain, “A note on distance-weighted k-nearest neighbor rules,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 4, pp. 311-313, 1978. doi: 10.1109/TSMC.1978.4309958
  4. V. B. Prasath et al., “Distance and similarity measures effect on the performance of k-nearest neighbor classifier - a review,“ arXiv:1708.04321 v3 [cs.LG], 2019. doi: 10.1089/big.2018.0175
  5. Y. Malkov, A. Ponomarenko, A. Logvinov, and V. Krylov, "Approximate nearest neighbor algorithm based on navigable small world graphs," Information Systems, vol. 45, pp. 61–68, 2014. doi: 10.1016/j.is.2013.10.006
  6. T. Kirdat and V. V. Patil, “Application of Chebyshev distance and Minkowski distance to CBIR using color histogram,” International Journal of Innovative Research in Technology (IJIRT), vol. 2, no. 9, pp. 28-31, 2016
  7. N. E. Md Isa, A. Amir, M. Z. Ilyas, and M. S. Razalli, “The performance analysis of k-nearest neighbors (K-NN) algorithm for motor imagery classification based on EEG signal, ” in 2017 International Conference on Emerging Electronic Solutions for IoT, Penang, Malaysia, Oct. 2017, pp. 1-6. doi: 10.1051/matecconf/201714001024
  8. N. Ali, D. Neagu, and P. Trundle, “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets,” SN Applied Sciences, vol. 1, no. 1559, 2019. doi: 10.1007/s42452-019-1356-9
  9. K. Chomboon, P. Chujai, P. Teerarassamee, K. Kerdprasop, and N. Kerdprasop, “An empirical study of distance metrics for k-nearest neighbor algorithm,” in the 3rd International Conference on Industrial Application Engineering 2015, Kitakyushu, Japan, Mar. 2015, pp. 1-6. doi: 10.12792/iciae2015.051
  10. L. Y. Hu, M. W. Huang, S. W. Ke, and C. F. Tsai, “The distance function effect on k-nearest neighbor classification for medical datasets,” SpringerPlus, vol. 5, no. 1, pp. 1-9, 2016. doi: 10.1186/s40064-016-2941-7
  11. P. Mulak and N. Talhar, “Analysis of distance measures using k-nearest neighbor algorithm on kdd dataset,” International Journal of Science and Research, vol. 4, no. 7, pp.2101-2104, 2015
  12. D. Sinwar and R. Kaushik, “Study of Euclidean and Manhattan distance metrics using simple k-means clustering,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 2, no. 5, pp. 270-274, 2014
  13. R. Todeschini, D. Ballabio, V. Consonni, and F. Grisoni, “A new concept of higher-order similarity and the role of distance/similarity measures in local classification methods,” Chemometrics and Intelligent Laboratory Systems, vol. 157, pp. 50-57, 2016. doi: 10.1016/j.chemolab.2016.06.013
  14. Y. Shikhar, V. P. Singh, and R. Srivastava, “Comparative analysis of distance metrics for designing an effective content-based image retrieval system using colour and texture features,” International Journal of Image, Graphics, and Signal Processing, vol. 12, no. 7, pp. 58-65, 2017. doi: 10.5815/ijigsp.2017.12.07
  15. P. Koniusz, F. Yan, P. H. Gosselin, and K. Mikolajczyk, "Higher-order occurrence pooling for bags-of-words: visual concept detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 2, pp. 313–326, 2017. doi: 10.1109/TPAMI.2016.2545667

Last update: 2021-03-07 11:50:52

  1. Discrimination of civet coffee using visible spectroscopy

    Graciella Mae L Adier, Charlene A Reyes, Edwin R Arboleda. Jurnal Teknologi dan Sistem Komputer, 8 (3), 2020. doi: 10.14710/jtsiskom.2020.13734

  2. K-nearest neighbor performance for Nusantara scripts image transliteration

    Anastasia Rita Widiarti. Jurnal Teknologi dan Sistem Komputer, 8 (2), 2020. doi: 10.14710/jtsiskom.8.2.2020.150-156

Last update: 2021-03-07 11:50:52

No citation recorded.
Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Starting in 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that their article is original, written by the mentioned author(s), has never been published before, does not contain statements that violate the law, does not violate the rights of others, is subject to copyright that is held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to articles, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce articles for its own purposes,
  • The right to archive articles yourself (please read our deposit policy), and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of articles (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of its initial publication in this journal (Journal of Technology and Computer Systems).

If the article was prepared jointly by more than one author, each author submitting the manuscript warrants that they have been given permission by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agree to notify the co-authors of the terms of this policy. JTSiskom will not be held responsible for anything that may arise because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.

Authors should also understand that once published, their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available. The license of published articles (and additional data) will be governed by the Creative Commons Attribution license as currently featured on the Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.