Perbandingan Model Klasifikasi untuk Deteksi  Berita Hoaks Menggunakan LSTM, Naive Bayes, Random Forest, K-Means, dan Word2Vec

Authors

  • Laurenhia Salsabella Afrinza Universitas Nusantara PGRI Kediri Indonesia
  • Fariez Frimansyah Frimansyah Universitas Nusantara PGRI Kediri Indonesia
  • Shella Ayu Ardita Universitas Nusantara PGRI Kediri Indonesia
  • Vera Angelita Febriani Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/qmq3j747

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.

Keywords:

deteksi berita hoaks, klasifikasi teks, word2vec

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Published

2025-07-10

Issue

Vol. 9 No. 1 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

License

Copyright (c) 2025 Laurenhia Salsabella Afrinza, Fariez Frimansyah Frimansyah, Shella Ayu Ardita, Vera Angelita Febriani

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How to Cite

Perbandingan Model Klasifikasi untuk Deteksi  Berita Hoaks Menggunakan LSTM, Naive Bayes, Random Forest, K-Means, dan Word2Vec. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(1), 810-820. https://doi.org/10.29407/qmq3j747