Skripsi

KLASIFIKASI BERITA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)

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Penilaian

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In today's digital information age, the abundance of news available on the internet creates the need for an automated system that can classify news very accurately based on its category. This research presents an approach that uses Long Short-Term Memory (LSTM) to classify news and Term Frequency-Inverse Document Frequency (TF-IDF) as word weighting. The LSTM method is used to process news text and extract features that represent relevant news content. The data used is multiclass and taken from Kaggle with a total data of 32,259 news titles which are then divided into 70% training data and 30% test data. After testing the test data, the LSTM classification performance results are obtained with an accuracy value of 87%, precision 88%, recall 87%, and f-score 88%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407001321	T140149	T1401492024	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1401492024

Publisher

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

Collation

xvii, 74 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.507

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Teori komunikasi dan kontrol
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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