Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning


  • Ernest Lim Institut Sains dan Teknologi Terpadu Surabaya
  • Esther Irawati Setiawan Institut Sains dan Teknologi Terpadu Surabaya
  • Joan Santoso Institut Sains dan Teknologi Terpadu Surabaya



Bahasa Indonesia, Deep Learning, fastText, Media Sosial, Stance Classification


Misinformasi merupakan fenomena yang semakin sering terjadi di media sosial, tidak terkecuali Facebook, salah satu media sosial terbesar di Indonesia. Beberapa penelitian telah dilakukan mengenai teknik identifikasi dan klasifikasi stance di media sosial Indonesia. Akan tetapi, penggunaan Word2Vec sebagai word embedding dalam penelitian tersebut memiliki keterbatasan pada pengenalan kata baru. Hal ini menjadi dasar penggunaan fastText embedding dalam penelitian ini. Dengan menggunakan pendekatan deep learning, penelitian berfokus pada performa model dalam klasifikasi stance suatu judul post kesehatan di Facebook terhadap judul post lainnya. Stance berupa for (setuju), observing (netral), dan against (berlawanan). Dataset terdiri dari 3500 judul post yang terdiri dari 500 kalimat klaim dengan enam kalimat stance terhadap setiap klaim. Model dengan fastText pada penelitian ini mampu menghasilkan F1 macro score sebesar 64%.


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How to Cite

E. Lim, E. I. Setiawan, and J. Santoso, “Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning”, INSYST, vol. 1, no. 2, pp. 65–73, Dec. 2019.