Pendeteksian berita palsu menggunakan RoBERTa dengan Optimalisasi Word Embedding
Abstract
Penyebaran berita palsu (hoax) telah menjadi permasalahan serius yang mempengaruhi opini publik dan menciptakan polarisasi di masyarakat. Penelitian ini bertujuan untuk mendeteksi berita palsu menggunakan model RoBERTa yang dioptimalkan dengan tiga teknik word embedding. Word embedding yang digunakan adalah RoBERTa, Word2Vec, dan GloVe. Dataset yang digunakan adalah "Indonesian fact and hoax political news" yang diambil dari Kaggle, Dataset ini memerlukan tahap pre-processing untuk membersihkan ketidakkonsistenan data, seperti mengubah singkatan menjadi kata lengkap dan menghapus tanda baca. Selanjutnya, dilakukan representasi teks menggunakan tiga metode word embedding yaitu Word2Vec, GloVe, dan RoBERTa. Proses pelatihan model dilakukan dengan validasi silang K-Fold untuk meningkatkan generalisasi model. Hasil penelitian menunjukkan bahwa embedding RoBERTa mencapai akurasi terbaik 96%, sedangkan word embedding Word2Vec mendapatkan akurasi 94%. Word Embedding Glove menunjukkan performa paling rendah dengan akurasi 51%. Penelitian ini membuktikan bahwa pemilihan teknik word embedding yang tidak tepat untuk model RoBERTa dapat mengurangi akurasi dan efektivitas model dalam mendeteksi berita palsu. Diharapkan bahwa temuan dalam penelitian ini dapat memberikan kontribusi terhadap peningkatan sistem deteksi berita palsu di masa mendatang.
Kata kunci: hoax, RoBERTa, GloVe, Word2Vec
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