KLASIFIKASI EMOSI PADA KALIMAT DI TWITTER MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY

RAMADHANTI, SHABRINA and Rini, Dian Palupi and Rodiah, Desty (2023) KLASIFIKASI EMOSI PADA KALIMAT DI TWITTER MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

Emotions have an important role in human life, especially in social interactions on social media platforms such as Twitter. The classification of emotions in an important sentence plays a role in analyzing public opinion and can help in decision making. The Long Short-Term Memory (LSTM) algorithm is a refinement of the Recurrent Neural Network (RNN) algorithm which is able to manage sequential data, overcome dependencies between words in a sentence, and control information in memory such as remembering important information and deleting irrelevant information. Therefore, LSTM is an effective method to use in the process of classifying emotions in a sentence. The data used in this research amounted to 1000 data where there were 5 emotion classes with equal numbers. The model creation process was carried out using several tests by tuning the learning rate and batch size parameters. The research results show that the learning rate = 0.001 and batch size = 16 produce the best performance with accuracy and recall values of 77% and precision and f1-score values of 76.8%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi emosi, Long Short-Term Memory
Subjects:	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:	Shabrina Ramadhanti
Date Deposited:	11 Jan 2024 08:12
Last Modified:	11 Jan 2024 08:12
URI:	http://repository.unsri.ac.id/id/eprint/137933

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