Implementation of Enhance Confix Stripping Stemmer Algorithm for Multiclass Dataset Classification in News Text using K-Nearest Neighbor

  • Alvianda Ricky Lukman Student
  • Widi Astuti

Abstract

Needs for news information has increased since the change from physical media to online media. News is grouped according to categories to making it easier for readers to get the news as desired. Grouping to determine the category of news information is known as text classification. The number of words in the news text create diversity of words that appear and can be minimized by the stemming process, which is changing an affixed word into its root word. This study comparing between use of stemming and without stemming and finding the best value of K and optimum distance calculation of K-Nearest Neighbor. The best accuracy is 0.9671 which is obtained when stemming algorithm not applied, number of K=9 and cosine distance is used as distance metric. This result is greater than the classification that applies stemming algorithm in condition K=7 using cosine distance which resulted accuracy in 0.9660.

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Published
2021-10-07
How to Cite
Lukman, A., & Astuti, W. (2021, October 7). Implementation of Enhance Confix Stripping Stemmer Algorithm for Multiclass Dataset Classification in News Text using K-Nearest Neighbor. Journal of Data Science and Its Applications, 4(1), 11 - 17. https://doi.org/https://doi.org/10.34818/jdsa.2021.4.76