DETEKSI BERITA HOAX BERBAHASA INDONESIA MENGGUNAKAN ALGORITMA BERT DAN LSTM

FAHMI, DZUL (2023) DETEKSI BERITA HOAX BERBAHASA INDONESIA MENGGUNAKAN ALGORITMA BERT DAN LSTM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Today, Information and Communication Technology (ICT) is developing very rapidly. Various media, one of which is social media, is a forum for disseminating information that is very influential in people's lives. The dissemination of information or news through social media is currently not only carried out by trusted news sites but also by all internet users. But unfortunately a lot of information or what is spread on social media cannot be justified for its truth or is called a hoax. This study aims to create a hoax news detection system which will later be classified by the Bidirectional Encoder Representations from Transformers (BERT) Algorithm and Long Short-Term Memory (LSTM), then the results of the classification are 2 classes, namely negative and positive classes. The research method used is data collection, data cleaning and classification. The results of the performance analysis using the BERT algorithm get the best results with an accuracy rate of 84%, 74% precision, 72% recall, and 73% f1-score. While the LSTM algorithm gets the best results with an accuracy rate of 83%, 71% precision, 67% recall and 68% f1-score. Key words: hoax news, BERT, LSTM Dewasa ini, Teknologi Informasi dan Komunikasi (TIK) berkembang sangat pesat. Beragam media salah satunya media sosial menjadi salah satu wadah penyebaran informasi yang sangat berpengaruh kepada kehidupan masyarakat. Penyebaran informasi atau berita melalui media sosial saat ini tidak hanya dilakukan oleh situs berita terpercaya namun juga oleh semua pengguna internet. Namun sayangnya banyak informasi atau yang disebarkan di media sosial tidak dapat dipertanggungjawabkan kebenerannya atau disebut dengan hoax. Penelitian ini bertujuan untuk membuat suatu sistem deteksi berita hoax yang nantinya diklasifikasikan oleh Algoritma Bidirectional Encoder Representations from Transformers (BERT) dan Long Short-Term Memory (LSTM), kemudian hasil klasifikasi tersebut terdapat 2 kelas yaitu kelas negatif dan positif. Metode penelitian yang digunakan yaitu pengumpulan data, pembersihan data dan klasifikasi. Hasil performa analisis menggunakan algoritma BERT mendapatkan hasil terbaik dengan tingkat akurasi 84%, presisi 74%, recall 72%, dan f1-score 73%. Sedangkan algoritma LSTM mendapatkan hasil terbaik dengan tingkat akurasi 83%, presisi 71%, recall 67% dan f1-score 68%. Kata kunci: berita hoax, BERT, LSTM

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