KLASIFIKASI MULTILABEL KOMENTAR PADA TWITTER MENGGUNAKAN LONG SHORT TERM MEMORY

ARMANDO, FADEL and Abdiansah, Abdiansah and Kurniati, Rizki (2023) KLASIFIKASI MULTILABEL KOMENTAR PADA TWITTER MENGGUNAKAN LONG SHORT TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

Twitter is one of the most popular social media platforms and users can write comments in the form of tweets freely without any restrictions. These tweets can contain various blasphemies and toxic comments. Toxic comments are comments that are rude, disrespectful, unreasonable, or even to the point of humiliating someone. They can cause serious problems on social media and some people will avoid engaging in unfair and unhealthy debates. Toxic comments can consist of several labels. This research aims to perform multilabel classification of comments. The method used is Long Short Term Memory and Word2Vec as word embedding. The data used amounted to 2,682 tweets which were then divided into 80% training data and 20% test data. After tuning the hyperparameters using random search, the best results were obtained for the LSTM model with a dropout configuration of 0.2, hidden unit 128, recurrent dropout in the LSTM layer 0.3, epochs 20, and batch size 64. Based on the research results, the average value of hamming loss is 0.138.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hamming Loss, Long Short Term Memory, Multilabel Classification, Twitter, Word2Vec
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Fadel Armando
Date Deposited:	21 Aug 2023 02:23
Last Modified:	21 Aug 2023 02:23
URI:	http://repository.unsri.ac.id/id/eprint/127461

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