IDENTIFICATION OF HOAX BASED ON TEXT MINING USING K-NEAREST NEIGHBOR METHOD

  • I Wayan Santiyasa
  • Gede Putra Aditya Brahmantha Universitas Udayana
  • I Wayan Supriana
  • I GA Gede Arya Kadyanan
  • I Ketut Gede Suhartana
  • Ida Bagus Made Mahendra

Abstract

At this time, information is very easy to obtain, information can spread quickly to all corners of society. However, the information that spreaded are not all true, there is false information or what is commonly called hoax which of course is also easily spread by the public, the public only thinks that all the information circulating on the internet is true. From every news published on the internet, it cannot be known directly that the news is a hoax or valid one. The test uses 740 random contents / issue data that has been verified by an institution, where 370 contents are hoaxes and 370 contents are valid. The test uses the K-Nearest Neighbor algorithm, before the classification process is performed, the preprocessing stage is performed first and uses the TF-IDF equation to get the weight of each feature, then classified using K-Nearest Neighbor and the test results is evaluated using 10-Fold Cross Validation. The test uses the k value with a value of 2 to 10. The optimal use of the k value in the implementation is obtained at a value of k = 4 with precision, recall, and F-Measure results of 0.764856, 0.757583, and 0.751944 respectively and an accuracy of 75.4%

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Published
2022-01-04
How to Cite
SANTIYASA, I Wayan et al. IDENTIFICATION OF HOAX BASED ON TEXT MINING USING K-NEAREST NEIGHBOR METHOD. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 10, n. 2, p. 217-226, jan. 2022. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/78507>. Date accessed: 30 may 2024. doi: https://doi.org/10.24843/JLK.2021.v10.i02.p04.

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