KLASIFIKASI HATE SPEECH DAN ABUSIVE LANGUAGE PADA TEKS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)

SARI, NADYA ANDRIANI PUSPITA and Utami, Alvi Syahrini and Rizqie, M. Qurhanul (2023) KLASIFIKASI HATE SPEECH DAN ABUSIVE LANGUAGE PADA TEKS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM). Undergraduate thesis, Sriwijaya University.

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Abstract

Social media is an online platform that allows users to share content, interact with other users, create statuses, and also leave comments. Users can freely make comments that contain hate speech and abusive language. One platform that is widely used to make these comments is Twitter. This research aims to classify hate speech and abusive language in text. The method used is Long Short Term Memory (LSTM) and Word2Vec as word embedding. The data used is multilabel class and taken from Kaggle with a total data of 13,169 tweets which are then divided into 80% training data and 20% test data. After manually searching for random hyperparameters 10 times for each hyperparameter, the best results were obtained for the LSTM model with a dropout configuration of 0.2, hidden unit 256, recurrent dropout in the LSTM layer 0.2, epochs 15, and batch size 32. After the research, the average hamming loss value was 0.153.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hamming Loss, Klasifikasi Multilabel, Long Short Term Memory, Twitter, Word2Vec, Hate Speech, Abusive Language
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:	Nadya Andriani Puspita Sari
Date Deposited:	02 Jan 2024 13:05
Last Modified:	02 Jan 2024 13:05
URI:	http://repository.unsri.ac.id/id/eprint/137346

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