The Sriwijaya University Library

  • Home
  • Information
  • News
  • Help
  • Librarian
  • Login
  • Member Area
  • Select Language :
    Arabic Bengali Brazilian Portuguese English Espanol German Indonesian Japanese Malay Persian Russian Thai Turkish Urdu

Search by :

ALL Author Subject ISBN/ISSN Advanced Search

Last search:

{{tmpObj[k].text}}
Image of ANALISIS SENTIMEN KOMENTAR VAKSINASI COVID-19 DI INSTAGRAM MENGGUNAKAN DEEP LEARNING XLNET

Text

ANALISIS SENTIMEN KOMENTAR VAKSINASI COVID-19 DI INSTAGRAM MENGGUNAKAN DEEP LEARNING XLNET

Rafi, Muhammad - Personal Name;

Penilaian

0,0

dari 5
Penilaian anda saat ini :  

The Covid-19 vaccination activity is one of the most discussed things on social media. Comments on social media can be used to look at public sentiment on the Covid-19 vaccination. This study aims to perform sentiment analysis on Instagram comments about Covid-19 vaccination using XLNet and look at its performance. This study used Wikipedia corpus data and 2.000 Indonesian comments from Instagram. This study uses two software, namely training and test software. Training software is used to pre-train and fine-tune the XLNet model. Test software used for testing sentiment analysis using XLNet. The results of sentiment analysis on Instagram comments using XLNet get the best accuracy value on the model using epochs value: 7 and batch size: 16 in the fine-tuning process with accuracy: 74,25%, precision: 70,73%, recall: 82,75%, and f-measure: 76,27%. This study found that epoch value and batch size changes in the fine-tuning process affect the model performance. The epoch value addition and the selection of a smaller batch size value in the fine-tuning process almost always increase the model accuracy. The epochs value addition reduces accuracy in model with epochs value: 8 and batch size: 16, also in model with epochs value: 7 and batch size: 50. Meanwhile, the selection of a smaller batch size reduces accuracy in model with epochs value: 3 and batch size: 32 as well as a model with epochs value: 8 and batch size: 16.


Availability
Inventory Code Barcode Call Number Location Status
2207004222T79439T794392022Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T794392022
Publisher
Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022
Collation
xvii, 93 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.754 07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Teknik Informatika
Situs Jejaring Sosial
Specific Detail Info
-
Statement of Responsibility
SEPTA
Other version/related

No other version available

File Attachment
  • ANALISIS SENTIMEN KOMENTAR VAKSINASI COVID-19 DI INSTAGRAM MENGGUNAKAN DEEP LEARNING XLNET
Comments

You must be logged in to post a comment

The Sriwijaya University Library
  • Information
  • Services
  • Librarian
  • Member Area

About Us

As a complete Library Management System, SLiMS (Senayan Library Management System) has many features that will help libraries and librarians to do their job easily and quickly. Follow this link to show some features provided by SLiMS.

Search

start it by typing one or more keywords for title, author or subject

Keep SLiMS Alive Want to Contribute?

© 2025 — Senayan Developer Community

Powered by SLiMS
Select the topic you are interested in
  • Computer Science, Information & General Works
  • Philosophy & Psychology
  • Religion
  • Social Sciences
  • Language
  • Pure Science
  • Applied Sciences
  • Art & Recreation
  • Literature
  • History & Geography
Icons made by Freepik from www.flaticon.com
Advanced Search