PENERAPAN METODE LONG-SHORT TERM MEMORY DAN FASTTEXT PADA KLASIFIKASI UJARAN KEBENCIAN

WARDANI, RACHMANIAH KESUMA and Rodiah, Desty (2024) PENERAPAN METODE LONG-SHORT TERM MEMORY DAN FASTTEXT PADA KLASIFIKASI UJARAN KEBENCIAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Hate speech on digital platforms poses a serious threat to social cohesion as it can lead to discrimination, conflict, and violence. This highlights the importance of developing automated detection systems to mitigate its impact and create safer digital spaces. To address this challenge, this study aims to develop a hate speech classification model in the Indonesian language using the Long Short-Term Memory (LSTM) method combined with the Fasttext Word Embedding approach. Fasttext was chosen for its ability to capture sub-word meanings and handle spelling variations, while LSTM excels in processing sequential data and understanding textual context comprehensively. The dataset used in this study comprises 2,980 social media comments categorized into two classes: hate speech and non-hate speech. The best-performing model, configured with 128 neurons in the LSTM layer, a dropout rate of 0.7, a learning rate of 0.1, a batch size of 64, and trained for 30 epochs, achieved an accuracy of 92.24%, a precision of 92.58%, a recall of 92.58%, and an F1-score of 92.58%. These results demonstrate that the developed model exhibits strong performance in classifying comments with high accuracy and robust generalization capabilities.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ujaran kebencian, LSTM, FastText, Klasifikasi
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Rachmaniah Kesuma Wardani
Date Deposited:	08 Jan 2025 02:44
Last Modified:	08 Jan 2025 02:44
URI:	http://repository.unsri.ac.id/id/eprint/162919

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