Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Rianansyah, Anggit
Unknown Affiliation
Computer Science & IT Education

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

Found 1 Documents
Search

 1 
IMPLEMENTATION OF WORD EMBEDDING IN DETECTING POLITICAL FAKE NEWS IN INDONESIA USING LONG SHORT-TERM MEMORY ALGORITHM Rianansyah, Anggit; Utami, Ema; Ariatmanto, Dhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6680

Abstract

This research investigates the use of word embedding techniques to detect political fake news in Indonesia by utilizing the Long Short-Term Memory (LSTM) algorithm. The spread of fake news, particularly in the political realm, poses significant challenges to public trust and the integrity of information. To address these challenges, we employed a dataset of political news articles and applied word embedding to convert the text into a numerical format that represents the semantic relationships between words. The LSTM algorithm, known for its ability to process and learn from sequential data, was then used to identify patterns indicative of fake news. Our model demonstrated satisfactory accuracy, with the LSTM algorithm without word embedding achieving an average accuracy of 67%, while the application of word embedding (Word2Vec, Glove, and FastText) resulted in average accuracies of 84%, 81%, and 86%, respectively. These findings confirm that combining word embedding with LSTM is effective in detecting fake news. This research contributes to ongoing efforts to combat misinformation in Indonesia by providing a robust tool to enhance the reliability of news in the digital age. Further developments, such as the integration of additional linguistic features and the expansion of the dataset, are expected to improve the model’s performance and adaptability across various contexts.
Co-Authors DHANI ARIATMANTO Ema Utami