PEMODELAN TOPIK MENGGUNAKAN PRE-TRAINED LANGUAGE MODEL ROBERTA DAN VARIATIONAL AUTOENCODER

MUWAFA, FADHIL ZAHRAN and Yusliani, Novi and Rachmatullah, Muhammad Naufal (2024) PEMODELAN TOPIK MENGGUNAKAN PRE-TRAINED LANGUAGE MODEL ROBERTA DAN VARIATIONAL AUTOENCODER. Undergraduate thesis, Sriwijaya University.

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Abstract

The rapid and widespread flow of information highlights the importance of efficient text data management, making it even more important to organize and classify information from text data as more news is published online all the time. Topic modeling is useful in clustering news texts from the ever-growing sea of online news based on the topic of each text data. One method of topic modeling is to use Variational Autoencoder combined with a trained language model, RoBERTa. This research aims to create a topic modeling system using the Pre-trained Language Model RoBERTa and Variational Autoencoder. The dataset used consists of 5000 news data with 10 different topics taken from cnnindonesia, kompas, and detik.com. Topic modeling evaluation is done using coherence score cv, homogeneity score, and v-measure. With a coherence score cv of 77.3%, homogeneity score of 6.5%, and v-measure of 7.1%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pemodelan Topik, Variational Autoencoder, Pre-trained Language Model, RoBERTa, Coherence Score cv, Homogeneity Score, V-Measure
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.6.E9 Management information systems -- Congresses.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Fadhil Zahran Muwafa
Date Deposited:	25 Apr 2024 08:30
Last Modified:	01 Jul 2024 04:38
URI:	http://repository.unsri.ac.id/id/eprint/143437

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