Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Bagas Pradipabista Nayoga
Bina Nusantara University
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Hoax analyzer for Indonesian news using RNNs with fasttext and glove embeddings Ryan Adipradana; Bagas Pradipabista Nayoga; Ryan Suryadi; Derwin Suhartono
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2956

Abstract

Misinformation has become an innocuous yet potentially harmful problem ever since the development of internet. Numbers of efforts are done to prevent the consumption of misinformation, including the use of artificial intelligence (AI), mainly natural language processing (NLP). Unfortunately, most of natural language processing use English as its linguistic approach since English is a high resource language. On the contrary, Indonesia language is considered a low resource language thus the amount of effort to diminish consumption of misinformation is low compared to English-based natural language processing. This experiment is intended to compare fastText and GloVe embeddings for four deep neural networks (DNN) models: long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), gated recurrent unit (GRU) and bidirectional gated recurrent unit (BI-GRU) in terms of metrics score when classifying news between three classes: fake, valid, and satire. The latter results show that fastText embedding is better than GloVe embedding in supervised text classification, along with BI-GRU + fastText yielding the best result.
Co-Authors Derwin Suhartono Ryan Adipradana Ryan Suryadi