Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Authors

DOI:

https://doi.org/10.29407/intensif.v8i1.21971

Keywords:

Question Answering, NLP, Transformer, IndoBERT, RoBERTa

Abstract

The campus introduction process is a stage where new students acquire information about the campus through a series of activities and interactions with existing students. However, the delivery of campus introduction information is still limited to conventional methods, such as using guidebooks. This limitation can result in students having a limited understanding of the information needed during their academic period. The one of solution for this case is to implement a deep learning system with knowledge-based foundations. This research aims to develop a Question Answering System (QAS) as a campus introduction guide by comparing two transformer methods, namely the RoBERTa and IndoBERT architectures. The dataset used is processed in the SQuAD format in the Indonesian language. The collected SQuAD dataset in the Indonesian language consists of 5046 annotated data. The result shows that IndoBERT outperforms RoBERTa with EM and F1-Score values of 81.17 and 91.32, respectively, surpassing RoBERTa with EM and F1-Score values of 79.53 and 90.18.

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Author Biographies

Fedryanto Dartiko, Universitas Bengkulu

Department Informatics Universitas Bengkulu

Mochammad Yusa, Universitas Bengkulu

Department Informatics Universitas Bengkulu

Aan Erlansari, Universitas Bengkulu

Department Information System Universitas Bengkulu

Shaikh Ameer Basha, Bearys Institute of Technology

Department of Mathematics
Bearys Institute of Technology, Mangalore-574199 India

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Published

2024-02-10

How to Cite

[1]
F. Dartiko, M. Yusa, A. Erlansari, and S. A. Basha, “Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides”, INTENSIF: J. Ilm. Penelit. dan Penerap. Tek. Sist. Inf., vol. 8, no. 1, pp. 126–143, Feb. 2024.