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 PERBANDINGAN KLASIFIKASI INTENT CHATBOT MENGGUNAKAN DEEP LEARNING BERT, ROBERTA, DAN INDOBERT

Skripsi

ANALISIS PERBANDINGAN KLASIFIKASI INTENT CHATBOT MENGGUNAKAN DEEP LEARNING BERT, ROBERTA, DAN INDOBERT

Dwiyono, Aswin - Personal Name;

Penilaian

0,0

dari 5
Penilaian anda saat ini :  

A chatbot is a software application to designed handle user inputs and generate appropriate replies based on those inputs, which are then communicated back to the user. In able to provide accurate responses, the chatbot must be able to understand the intent of the user accurately. An issue in the development of chatbots is how to accurate classify user intent. Incorrectly understanding user intent can result in irrelevant responses. In order to have a conversation with the user, the intent of the user needs to be classified correctly. This paper compares three state-of-the-art transformer-based models BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and IndoBERT (Indonesia Bidirectional Encoder Representations from Transformer) for the task of intent classification in chatbot systems. Various performance metrics, including accuracy, F1-score, precision, and recall, were analyzed to determine which model performs more effectively in the same parameter conditions. Performance metrics like accuracy and F1-score were compared to assess model BERT, RoBERTa and IndoBERT performs better in a University Chatbot Dataset in Indonesian language. The BERT model achieved an accuracy of 0.89, RoBERTa model achieved 0.84 and IndoBERT model achieved an accuracy of 0.94. The better performance of IndoBERT compared to BERT and RoBERTa is caused by more language-specific training, more relevant pretraining, and more effective adaptation to Indonesian context and structure.


Availability
Inventory Code Barcode Call Number Location Status
2507000263T164391T1643912025Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
,
Call Number
T1643912025
Publisher
Indralaya : Prodi Magister Ilmu Komputer, Fakultas Ilmu Komputer., 2024
Collation
xii, 65 hlm., ilus., tab.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Magister Ilmu Komputer
Pemrosesan Data, Teknik Informatika
Specific Detail Info
-
Statement of Responsibility
TUTI
Other version/related

No other version available

File Attachment
  • ANALISIS PERBANDINGAN KLASIFIKASI INTENT CHATBOT MENGGUNAKAN DEEP LEARNING BERT, ROBERTA, DAN INDOBERT
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