Comparative Analysis of Indonesian Text Mining News Online Classification Using the K-Nearest Neighbor and Random Forest Algorithm

Authors

  • Oloan Sihombing Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Sarah Tri Yosepha Sitorus Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Evta Indra Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Stiven Hamonangan Sinurat Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Palma Juanta Universitas Prima Indonesia Medan

DOI:

https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2824

Abstract

The rapid development of internet technology today makes many news media grow pretty rapidly. Newspaper companies have utilized internet technology to spread the latest news online through online mass media. Hundreds of thousands of stories are written and published daily on online-based Indonesian news portals, making it difficult for readers to find the news topics they want to read. In making it easier for readers to find the news they are looking for, news needs to be classified according to its respective categories, such as education, current news, finance, and sports. So to classify categories, a text classification method is needed or often called Text Mining. Text mining is a data mining classification technique for processing text using a computer to produce helpful text analysis. In this study, a comparison of 2 methods for developing texts was carried out to get accuracy above 80%.

Downloads

Published

2022-08-22

How to Cite

[1]
O. Sihombing, S. T. Y. Sitorus, E. Indra, S. H. Sinurat, and P. Juanta, “Comparative Analysis of Indonesian Text Mining News Online Classification Using the K-Nearest Neighbor and Random Forest Algorithm”, JUSIKOM PRIMA, vol. 6, no. 1, pp. 49-55, Aug. 2022.

Issue

Vol. 6 No. 1 (2022): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)

Section

Articles

License

Copyright (c) 2022 Oloan Sihombing, Sarah Tri Yosepha Sitorus, Evta Indra, Stiven Hamonangan Sinurat, Palma Juanta

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:

  • Copyright to the manuscripts of scientific papers in this Journal is held by the author.
  • The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
  • Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
  • Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.