KLASIFIKASI EMOSI TEKS SINGKAT MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)

OKTAVIAN, MUHAMMAD RAFI and Yusliani, Novi and Satria, Hadipurnawan (2025) KLASIFIKASI EMOSI TEKS SINGKAT MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM). Undergraduate thesis, Sriwijaya University.

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Abstract

Short text messages, such as those found on social media and messaging platforms, often reflect the emotional expressions of users. Emotion clasification in these texts has significant potential for applications such as sentiment analysis and content personalization. This study aims to develop a short text emotion clasification model using the Long Short-Term Memory (LSTM) method. The dataset used was obtained from the Hugging Face platform, consisting of 18,358 labeled sentences categorized into five emotions: sadness, joy, anger, fear, and surprise. The data underwent a pre-processing phase, followed by word embedding using Word2Vec before being input into the emotion clasification model. The evaluation of the model was carried out under several experimental configurations to determine optimal performance. The best results were achieved with an LSTM configuration using 128 neurons, a dropout of 0.2, a learning rate of 0.001, batch size of 16, and 10 epochs. The developed model achieved an accuracy of 87.61%, a precision of 88.31%, a recall of 87.61%, and an F1-score of 87.81%, indicating its effectiveness in detecting emotions from short text.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Emosi, Long Short-Term Memory
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Rafi Oktavian
Date Deposited:	25 Jul 2025 14:17
Last Modified:	25 Jul 2025 14:17
URI:	http://repository.unsri.ac.id/id/eprint/180651

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