Optimizing hate speech detection in the Indonesian language using FastText and LSTM algorithms.

Wenando, Febby Apri and Yusoff, Nooraini and Md Saleh, Nurul Izrin (2024) Optimizing hate speech detection in the Indonesian language using FastText and LSTM algorithms. In: 2nd International Symposium on Information Technology and Digital Innovation: Creative Trends in Sustainable Information Technology Design and Innovation, ISITDI 2024.

Text Optimizing Hate Speech Detection in the Indonesian Language using FastText and LSTM Algorithms.pdf Download (513kB)

Abstract

The term hate speech encompasses communication that conveys hostility or prejudice toward individuals or groups based on specific attributes, including race, ethnicity, gender, or religion. Given the prevalence of such harmful content on major social media platforms like Twitter, it is imperative to prioritize ongoing research into automated detection methods. This study employs advanced artificial intelligence and machine learning techniques, involving pivotal stages such as data collection, preprocessing, oversampling, fastText word embedding, and classification using Long Short- Term Memory (LSTM) networks to achieve the objective. The amalgamation of fastText embeddings with LSTM classification yielded noteworthy results, with the model achieving 94% accuracy, 92% precision, 94% recall, and a 93% F1 score. These outcomes underscore the effectiveness of sophisticated machine learning methodologies in accurately discerning and addressing the proliferation of hate speech online, particularly in the context of Indonesian-language content.

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Information and Communication Technology
Depositing User:	NURHASHIRAH BORHAN
Date Deposited:	31 Jul 2026 07:57
Last Modified:	31 Jul 2026 07:57
URI:	http://eprints.utem.edu.my/id/eprint/30145
Statistic Details:	View Download Statistic

Actions (login required)

View Item