Model Deteksi Berita Palsu Menggunakan Pendekatan Bidirectional Long Short Term Memory (BiLSTM)
DOI:
10.51519/journalcisa.v4i2.397Keywords:
Berita palsu, Embeddings GloVe, Bidirectional Long Short Term Memory (BiLSTM)Abstract
Isu berita palsu telah menarik perhatian masyarakat dan akademisi. Penyebaran informasi yang tidak akurat berpotensi mengubah pandangan publik dan memungkinkan manipulasi opini. Dalam konteks data yang melimpah, kami mengembangkan model untuk mendeteksi berita palsu dengan mengklasifikasikan fitur linguistik murni. Dengan pendekatan pembelajaran mendalam, kami mengevaluasi respons terhadap artikel tertentu menggunakan model Jaringan Saraf Rekuren Bidirectional Long Short Term Memory (BiLSTM) dan representasi kata dari embeddings GloVe. Hasil evaluasi menunjukkan adaptabilitas model pada data latih dengan kerugian terendah 0.30% dan akurasi tinggi 99.14%. Gabungan antara embeddings GloVe dan Bi-LSTM memunculkan hasil yang positif. Penelitian ini memiliki potensi untuk memberikan kontribusi dalam penanggulangan penyebaran berita palsu yang semakin meresahkan di berbagai bidang.
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