Sentiment Analysis of DLU Ferry Application Reviews on the Google Play Store Using Bidirectional Encoder Representations from Transformers
DOI:
https://doi.org/10.30649/japk.v13i2.94Keywords:
Sentiment Analysis, DLU Ferry, Google Play Store, BERT, hyperparameterAbstract
DLU Ferry is an application issued by PT. Dharma Lautan Utama to make it easier for customers to order tickets. The DLU Ferry application still has weaknesses, therefore it needs improvement to improve the quality of this application. To find out the weaknesses of this application can be obtained from reviews written by consumers on the Google Play Store. This research contains analysis of consumer review data on the DLU Ferry application. This study uses data from 1575 reviews consisting of positive, neutral and negative sentiments. This research will measure the performance of the Bidirectional Encoder Representations from Transformers (BERT) method in classifying sentiments using the IndoBERT-base Pretrained model with fine-tuning techniques. The test results in this study obtained an accuracy of 86% with the selection of hyperparameters, namely batch size 32, learning rate 3e-6, and epoch 5.
References
Atmaja, Raden Mas Rizqi Wahyu Panca Kusuma; Yustanti, Wiyli. (2021). Analisis Sentimen Customer Review Aplikasi Ruang Guru dengan
Metode BERT (Bidirectional Encoder Representations from Transformers). Journal of Emerging Information Systems and Business
Intelligence, Volume 02 Number 03, 2021.
Deepa Anand and Deepan Naorem. (2016). "Semi-supervised Aspect Based Sentiment Analysis for Movies Using Review Filtering," in
Procedia Computer Science, vol. 84, pp. 86–93.
Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina. (2019). BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding. Proceedings of NAACL-HLT 2019, pages 4171–4186.
Fatemeh Hemmatian and Mohammad Karim Sohrabi. (2019). A survey on classification techniques for opinion mining and sentiment
analysis. Artificial Intelligence Review 52, 1495–1545.
Pang, B. and Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2, 1-135.
Prananda, Alifia Revan & Thalib, Irfandy. (2020). Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion. Journal of
Information Systems Engineering and Business Intelligence. Vol. 6, No. 1.
Putra, Eza Ananda. (2021). Sentiment Analysis Using Bidirectional Encoder Representations from Transformers (BERT). Diakses dari
d691e0440
Rahul, V. Raj and Monika. (2019). "Sentiment Analysis on Product Reviews," 2019 International Conference on Computing,
Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 5-9.
Rauf, Saad Abdul; Qiang, Yan; Ali, Syed Basit; Ahmad, Waqas. (2019). Using BERT for Checking the Polarity of Movie Reviews.
International Journal of Computer Applications (0975 – 8887) Volume 177 – No. 21.
Saadah, Siti; Auditama, Kaenova Mahendra; Fattahila, Ananda Affan; Amorokhman Fendi Irfan; Aditsania, Annisa; Rohmawati, Aniq Atiqi.
(2022). Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia. Jurnal
Rekayasa Sistem dan Teknologi Informasi (RESTI). Vol. 6 No. 4 (2022) 648 – 655.
Wilie, B., Vincentio, K., Winata, G. I., Cahyawijaya, S., Li, X., Lim, Z. Y., Soleman, S., Mahendra, R., Fung, P., Bahar, S., and Purwarianti, A.
(2020). IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding. Proceedings of the 1st
Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference
on Natural Language Processing, pages 843–857.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ekka Pujo Ariesanto Akhmad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
