Keywords
klasifikasi teks
word2vec
How to Cite
Abstract
Penyebaran berita hoaks melalui media daring menjadi ancaman serius bagi masyarakat digital. Penelitian ini bertujuan untuk membandingkan performa berbagai model klasifikasi dalam mendeteksi berita hoaks, yaitu Long Short-Term Memory (LSTM), Naive Bayes, Random Forest, dan K-Means Clustering, dengan pemanfaatan Word2Vec sebagai metode representasi vektor kata. Dataset yang digunakan adalah kumpulan berita hoaks dan fakta yang telah tersedia secara publik. Hasil eksperimen menunjukkan bahwa LSTM memiliki akurasi tertinggi dalam mendeteksi berita hoaks, diikuti oleh Random Forest dan Naive Bayes, sedangkan K-Means kurang akurat karena merupakan metode unsupervised. Penelitian ini memberikan kontribusi terhadap pengembangan sistem deteksi berita hoaks otomatis yang andal.
References
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