Skripsi

TOPIC MODELING MENGGUNAKAN BERTOPIC DENGAN LLAMA2 SEBAGAI TOPIC REPRESENTATION TUNING

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Penilaian

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The growing use of social media, particularly Twitter, has generated large amounts of information in the form of text data covering a wide range of topics and issues. Analyzing Twitter data has the potential to find important insights into topics relevant to society. Topic modeling is one of the latest innovations in text data processing to find topics in a set of text. This research aims to perform topic modeling on Indonesian tweets using BERTopic with LLAMA2 as topic representation tuning. LLAMA2 is used to generate labels from a set of keywords generated from c-TF-IDF calculations. The dataset used consists of 10,000 Indonesian tweets taken from the Twitter account @detikcom. The data is divided into 2 parts, 8,000 tweets are used for training data and 2,000 tweets are used for testing. Based on the results of topic modeling with BERTopic, 49 total topics were obtained. Topic Modeling Evaluation is done using coherence score cv, obtained an average coherence score cv of 0.86 on training data and 0.73 on testing data.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003248	T145486	T1454862024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1454862024

Publisher

Indralaya : Fakultas Ilmu Komputer., 2024

Collation

xiii, VI-1 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

621.390 7

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Prodi Teknik Informatika
PEMODELAN TOPIK

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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