Comparative analysis of performance between multimodal implementation of chatbot based on news classification data using categories

Prasnurzaki Anki, Alhadi Bustamam, Rinaldi Anwar Buyung

Research output: Contribution to journalArticlepeer-review

Abstract

In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study.

Original languageEnglish
Article number2696
JournalElectronics (Switzerland)
Volume10
Issue number21
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • 1D CNN
  • 1D CNN transpose
  • Bi-GRU
  • Chatbot
  • GRU

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