Abstract
During Covid-19 pandemic, there was various hoax news about Covid-19. There are truth-clarification platforms for hoax news about Covid-19 such as Jala Hoax and Saber Hoax which categorize into misinformation and disinformation. Classification of supervised learning methods is applied to carry out learning from fact labels. Dataset is taken from Jala Hoax and Saber Hoax as many as 559 data which are made into Class 1 (Misleading Content, Satire/Parody, False Connection), Class 2 (False Context, Imposter Content), Class 3 (Fabricated and Manipulated Content). K-Nearest Neighbor (K-NN) is used to classify categories of misinformation and disinformation. Dissimilarity measure Jaccard Distance is compared with Euclidean, Manhattan, and Minkowski and uses k-value variance in the K-NN to determine the performance comparison results for each test. Results of Jaccard Distance at the value of k = 4 get a higher value than other model with an accuracy 0.696, precision 0.710, recall 0.572, and F1-Score. Maximum results tend to be on label of the most data class in Class 1 (Misleading Content, Satire or Parody, False Connection) with a total of 58 correct data from 61 test data.
Highlights
(Covid-19) sebagai wabah pandemi pada awal tahun 2020, penggunaan dan penerapan aplikasi daring bertambah digunakan dalam berbagai bidang untuk mencegah penyebaran Covid-19 karena dapat menjaga jarak atau mengurangi kerumunan
Salah satu algoritma dalam klasifikasi yaitu K-Nearest Neighbor (K-NN) yang melakukan klasifikasi berdasarkan data latih dan dihitung berdasarkan tetangga terdekatnya (Satrian & Gusrianty, 2020)
Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus. ILKOM Jurnal Ilmiah, 12(2), 104–111
Summary
Jaccard Distance beserta varian nilai k terhadap algoritma klasifikasi K-NN, karena dalam penelitian sebelumnya algoritma tersebut lebih unggul dibandingkan algoritma lainnya dalam menentukan label dan mempunyai berbagai model yang mempunyai hasil pengujian performa yang berbeda. K-NN serta membandingkan performa varian nilai k pada pengujian hasil evaluasi sehingga dengan pelaksanaan pengujian yang akan dilakukan dapat diketahui perbandingan performa dari penerapan model terhadap algoritma klasifikasi K-NN dalam menentukan pengelompokkan fakta berita hoaks mengenai Covid-19 berdasarkan kategori misinformasi dan disinformasinya. Digram Alir Penelitian Berdasarkan diagram alir pada Gambar 1 berikut ditampilkan penjelasan dari beberapa proses sehingga memperjelas dari diagram alir tersebut. Proses ini terkait dalam pengolahan kata yang didapatkan dari data fakta berita hoaks, proses tersebut mencakup case folding, tokenizing, filtering atau stopword removal (Wibawa, Nasrun, & Setianingsih, 2018).
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