Skripsi

KEYPHRASE EXTRACTION DENGAN MENGGUNAKAN PRE-TRAINED LANGUAGE MODELS BERT DAN TOPIC-GUIDED GRAPH ATTENTION NETWORKS

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Penilaian

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The increase in the amount of information and documents has made it difficult for individuals to search for information that matches relevant keywords. This poses a challenge in managing and accessing the required information. One solution to address this challenge is the use of Keyphrase Extraction Systems. These systems aim to generate keyphrase that represent the content of documents, enabling users to find documents based on relevant keywords related to the topic they are searching for. This research use an approach to keyphrase extraction that combines the use of pre-trained language models such as BERT and Topic-guided Graph Attention Networks. This method allows for the capture of semantic relationships between words in documents based on their topics. Empirical studies were conducted on a dataset containing 100 accredited scientific journal publications from Sinta 2 and Sinta 3 to evaluate the system's performance. The experimental results show a precision value of 0.058, a recall value of 0.070, and an F1-score of 0.062 for the five generated keywords across the entire tested dataset.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407000220	T137502	T1375022024	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1375022024

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvii, 107 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.507

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Program Aplikasi dengan Kegunaan Khusus

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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