Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002833	T14472	T1447252024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1447252024

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvii, xviv hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Prodi Teknik Informatika
Long Short Term Memory

Specific Detail Info

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Statement of Responsibility

UIN YOLA

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