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PENERAPAN SELF-ATTENTIVE NETWORK PADA PENDETEKSIAN CLICKBAIT DI JUDUL BERITA ONLINE INDONESIA

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Penilaian

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Clickbait is a term that describes a title with the aim of attracting the reader's interest by using flashy and provocative word choices. The problem faced if clickbait detection is done manually is that it takes a long time to be done, because it has to compare the content of the news by reading a whole news for each news title. This study intends to developed software that can detect whether a given news title is clickbait or not by using the self-attentive network method. The data used in this study were 6000 for training data and 2000 for test data. The data will enter the preprocessing stage before going to the self-attentive network. The data were trained using 10-fold Cross Validation. Based on the results of the test data, the model with the best performance is in the 4th fold with precision, recall, and f1-score values of 0.8057, 0.7960, and 0.8008. So that the accuracy value is 0.8020.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000850	T86890	T868902023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T868902023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xvi, 105 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

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