TEXT GENERATION MENGGUNAKAN LSTM PADA WEBSITE LINKEDIN

ASSABIL, MUHAMMAD RIZQI and Yusliani, Novi and Darmawahyuni, Annisa (2023) TEXT GENERATION MENGGUNAKAN LSTM PADA WEBSITE LINKEDIN. Undergraduate thesis, Sriwijaya University.

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Abstract

LinkedIn is one of the most popular sites out there to advertise oneself to potential employer. This study aims to create a good enough text generation model that it can generate a text as if it were made by someone who posts on LinkedIn. This study will use a Neural Network layer called Long-Short Term Memory (LSTM) as the main algorithm and the train data consists of actual posts made by users in LinkedIn. LSTM is an algorithm that is created to reduce vanishing and exploding gradient problem in Neural Network. From the result, final accuracy and loss varies. Increasing learning rate from its default value of 0.001, to 0.01, or even 0.1 creates worse model. Meanwhile, increasing dimensions of LSTM will sometimes increases training time or decreases it while not really increasing model performance. In the end, models chosen at the end are models with around 97% of accuracy that has a fairly stable learning graph and predicted output.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Jaringan Syaraf Tiruan, Long Short Term Memory, Natural Language Generation
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Rizqi Assabil
Date Deposited:	07 Feb 2023 01:32
Last Modified:	07 Feb 2023 01:32
URI:	http://repository.unsri.ac.id/id/eprint/89227

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