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PEMODELAN TOPIK MENGGUNAKAN BERTOPIC DENGAN KEYBERT UNTUK EKSTRAKSI KATA KUNCI SEBAGAI TOPIC REPRESENTATION TUNING

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In the era of technological advancement, the use of social media such as Twitter has become commonplace as a medium for online interaction. Every day, a vast number of tweets are generated by Twitter users from around the world. To determine which topics are trending, reading all the tweets on Twitter would take an extremely long time due to the sheer volume of tweets. One method used to efficiently extract information from Twitter tweets is topic modeling. Topic modeling is a method for discovering topics from various texts. This research aims to perform topic modeling on Indonesian-language tweets using BERTopic with KeyBERT for keyword extraction in each topic. KeyBERT will generate keywords for each topic cluster and will be used by BERTopic to enrich the results of the topic modeling. The dataset used consists of 10,000 Indonesian language tweets taken from the Twitter account @detikcom. The data is divided into two parts: 8,000 tweets are used for training data and 2,000 tweets are used for testing. Based on the topic modeling results with BERTopic, a total of 50 topics were obtained. Topic Modeling evaluation was conducted using coherence score, yielding an average of 0.765 on the training data and 0.675 on the testing data.

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Inventory Code Barcode Call Number Location Status
2407003793T147741T1477412024Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1477412024
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer.,
Collation
xiii, VI-2 hlm.; ilus.; tab.; 28 cm
Language
Indonesia
ISBN/ISSN
-
Classification
621.390 7
Content Type
Text
Media Type
unmediated
Carrier Type
unspecified
Edition
-
Subject(s)
Prodi Teknik Informatika
PEMODELAN TOPIK
Specific Detail Info
-
Statement of Responsibility
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No other version available

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