Image of PERINGKASAN TEKS BERITA DALAM BAHASA INDONESIA SECARA ABSTRAKTIF MENGGUNAKAN METODE ATTENTION-BASED BILSTM (BIDIRECTIONAL LONG SHORT TERM MEMORY)
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PERINGKASAN TEKS BERITA DALAM BAHASA INDONESIA SECARA ABSTRAKTIF MENGGUNAKAN METODE ATTENTION-BASED BILSTM (BIDIRECTIONAL LONG SHORT TERM MEMORY)

News is a form of information published to the public, allowing for a better understanding of the world around them. The advancement of internet technology has significantly increased the growth of Indonesian-language news sites and created a surge in information availability. Reading the entire news article takes a lot of time and makes it difficult to receive all the information quickly and accurately, leading to the concept of automatic text summarization. The BiLSTM (Bidirectional Long Short-Term Memory) algorithm is an artificial neural network architecture commonly used in text summarization tasks, especially with the addition of an attention layer that helps focus on important parts of the text. Abstractive summarization, which works by understanding and generating new sentences from the original document, is used in this study. Evaluations show that the BiLSTM model achieved a ROUGE-1 score of 0.0594, while the Hybrid model achieved a ROUGE-1 score of 0.1316.

Availability
#
Central Library (Reference) T1514882024
T151488
Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1514882024
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvi, 83 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.350 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Teknik Informatika
Kecerdasan Buatan – Metode BiLSTM
Specific Detail Info
-
Statement of Responsibility
Other version/related
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