KLASIFIKASI KOMENTAR BERACUN MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM)

AMRULLAH, IDHAM ATTA and Abdiansah, Abdiansah and Alfarissi, Alfarissi (2024) KLASIFIKASI KOMENTAR BERACUN MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM). Undergraduate thesis, Sriwijaya University.

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Abstract

The Kaggle platform, known as a hub for data and analytics competitions, provides a comprehensive dataset encompassing a range of comments, including toxic ones. Poisonous comments, often containing abusive, disrespectful, and demeaning language, impact the psychological well-being of individuals, particularly in the context of mental health. The presence of these comments on social media poses a serious challenge, as they not only disrupt healthy discussion but can also exacerbate mental health conditions. This study aims to classify toxic comments using the Long Short Term Memory. A total of 2,100 labeled data points were used, divided into two categories: toxic and non-toxi. The best LSTM model for classifying toxic comments had the optimal configuration with a learning rate of 0.0001, batch size of 8, 10 epochs, 32 neurons in the LSTM layer without LSTM dropout, and a dropout layer value of 0.2. With an accuracy of 85%, precision of 87.38%, recall of 82.95%, and f-measure of 85.11%, the model's effectiveness in classifying toxic comments is demonstrated.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Long Short Term Memory, Kaggle, Toxic Comments, Mental Health
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Idham Atta Amrullah
Date Deposited:	21 May 2024 02:16
Last Modified:	21 May 2024 02:16
URI:	http://repository.unsri.ac.id/id/eprint/144725

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