Deteksi Hoaks Pada Berita Berbahasa Indonesia Seputar COVID-19
DOI:
https://doi.org/10.22441/format.2021.v10.i1.007Kata Kunci:
web scraping, data mining, klasifikasi, feature engineering, hoaksAbstrak
Perkembangan teknologi yang semakin maju tentu mendatangkan banyak kemudahan bagi para penggunanya namun di lain sisi juga mempercepat penyebaran berita bohong pada internet. Berita bohong atau dikenal dengan hoaks adalah informasi sesat dan berbahaya karena menyesatkan persepsi manusia dengan menyampaikan informasi palsu sebagai kebenaran. Hoaks sendiri dapat bertujuan untuk mempengaruhi pembaca dengan informasi palsu sehingga pembaca mengambil tindakan sesuai dengan isi hoaks. Oleh karena itu, diperlukan sistem cerdas yang mampu mengklasifikasi sebuah berita dengan cepat yang menyebar melalui internet agar tidak menyesatkan para pembacanya. Penelitian ini dimulai dengan melakukan scraping berita yang sudah diberi kategori hoaks atau valid. Dataset tersebut dibagi dua menjadi data latih dan data uji. Dilakukan pre-processing mulai dari case folding, tokenizing, filtering dan stemming. Pada penelitian ini dilakukan perbandingan terhadap pengaruh penerapan feature engineering. Dari hasil akurasi, dapat dilihat bahwa dengan diterapkannya feature engineering mampu meningkatkan akurasi kelima metode klasifikasi. Metode random forest dengan penerapan feature engineering menghasilkan tingkat akurasi sebesar 96,05%.Unduhan
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