Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method
FAUZAAN RAKAN TAMA
Informasi Umum
Kode
23.04.2664
Klasifikasi
006.32 - Neural networks, perceptrons, connectionism, neural computers
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Natural Language Processing, Neural Networks,
Dilihat
306 kali
Informasi Lainnya
Abstraksi
<p>The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.</p>
- CII4E4 - TUGAS AKHIR
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | FAUZAAN RAKAN TAMA |
| Jenis | Perorangan |
| Penyunting | Yuliant Sibaroni |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
