Sentiment Analysis on Shopee Product Reviews Using IndoBERT

  • Suhardi Aras Muhammadiyah University of Sorong, Indonesia
  • Muhammad Yusuf Muhammadiyah University of Sorong, Indonesia
  • Reinhard Yohanis Ruimassa Muhammadiyah University of Sorong, Indonesia
  • Elli Agustinus Billi Wambrauw Muhammadiyah University of Sorong, Indonesia
  • Elsa Bura Pala'langan Muhammadiyah University of Sorong, Indonesia
DOI: 10.51519/journalisi.v6i3.814
Keywords: Marketplace, Shopee, Review, NLP, IndoBERT

Abstract

A marketplace is a place in cyberspace where there are commercial activities between buyers and sellers. Products offered from the marketplace have reviews to review. Shopee is the most visited marketplace by people and offers various products. Product reviews can provide benefits for other consumers in assessing the products offered. By utilizing NLP technology in particular, this study can classify positive sentiment and negative sentiment in product review data. The IndoBERT model is a model that can be used in NLP technology by utilizing the relationship between each input and output element as well as the weights to be calculated simultaneously. By utilizing this technology, sentiment analysis on Shopee product reviews provides maximum accuracy until 93% with different training conditions. This provide that IndoBERT model can show that the performance of the indoBERT model in this research is very good.

Downloads

Download data is not yet available.

References

E. H. Muktafin, K. Kusrini, and E. T. Luthfi, “Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Eksplora Inform., vol. 10, no. 1, pp. 32–42, 2020, doi: 10.30864/eksplora.v10i1.390.

N. A. Wardah and H. Harti, “Pengaruh Gaya Hidup Berbelanja Dan Promosi Penjualan Terhadap Pembelian Impulsif Avoskin Di Shopee,” Ecobisma (Jurnal Ekon. Bisnis Dan Manajemen), vol. 8, no. 2, pp. 145–166, 2021, doi: 10.36987/ecobi.v8i2.2090.

D. N. Sari, D. N. Sari, F. Adelia, F. Rosdiana, B. B. Butar, and M. Hariyanto, “Analisa Sentimen Terhadap Review Produk Kecantikan Menggunakan Metode Naive Bayes Classifier,” JIKA (Jurnal Inform., vol. 4, no. 3, p. 109, 2020, doi: 10.31000/jika.v4i3.3086.

T. Bey Kusuma, I. Komang, and A. Mogi, “Implementasi BERT pada Analisis Sentimen Ulasan Destinasi Wisata Bali,” J. Elektron. Ilmu Komput. Udayana, vol. 12, no. 2, pp. 409–420, 2023.

R. Merdiansah, S. Siska, and A. Ali Ridha, “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT,” J. Ilmu Komput. dan Sist. Inf., vol. 7, no. 1, pp. 221–228, 2024, doi: 10.55338/jikomsi.v7i1.2895.

S. Cahyawijaya et al., “IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation,” EMNLP 2021 - 2021 Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 8875–8898, 2021, doi: 10.18653/v1/2021.emnlp-main.699.

E. P. A. Akhmad, “Analisis Sentimen Ulasan Aplikasi DLU Ferry Pada Google Play Store Menggunakan Bidirectional Encoder Representations from Transformers,” J. Apl. Pelayaran Dan Kepelabuhanan, vol. 13, no. 2, pp. 104–112, 2023, doi: 10.30649/japk.v13i2.94.

W. Nurfitri and A. Chowanda, “Analisis Sentimen Pada Kasus Positif Covid-19 Berdasarkan Pemberitaan Media Di Indonesia Menggunakan Indobert,” Progresif J. Ilm. Komput., vol. 20, no. 1, p. 580, 2024, doi: 10.35889/progresif.v20i1.1897.

K. Cindy Pradhisa and R. Fajriyah, “Analisis Sentimen Ulasan Pengguna E-commerce di Google Play Store Menggunakan Metode IndoBERT,” Technol. Sci., vol. 6, no. 1, pp. 92–104, 2024, doi: 10.47065/bits.v6i1.5247.

Ardiansyah, Adika Sri Widagdo, Krisna Nuresa Qodri, F. E. N. Saputro, and Nisrina Akbar Rizky P, “Analisis sentimen terhadap pelayanan Kesehatan berdasarkan ulasan Google Maps menggunakan BERT,” J. Fasilkom, vol. 13, no. 02, pp. 326–333, 2023, doi: 10.37859/jf.v13i02.5170.

P. F. Supriyadi and Y. Sibaroni, “Xiaomi Smartphone Sentiment Analysis on Twitter Social Media Using IndoBERT,” J. Ris. Komputer), vol. 10, no. 1, pp. 2407–389, 2023, doi: 10.30865/jurikom.v10i1.5540.

R. Maulana Arrasyid, D. Enggar Putera, and A. Yunizar Pratama Yusuf, “Analisis Sentimen Review Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Tekno Kompak, vol. 18, no. 2, pp. 1–12, 2021.

A. Prabowo and F. Indra Sanjaya, “Penerapan Metode Transfer Learning Pada Indobert Untuk Analisis Sentimen Teks Bahasa Jawa Ngoko Lugu,” J. Sist. Inf. dan Sist. Komput., vol. 9, no. 2, pp. 205–217, 2024, [Online]. Available: http://e-jurnal.stmikbinsa.ac.id/simkom

B. Juarto and Yulianto, “Indonesian News Classification Using IndoBert,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 2, pp. 454–460, 2023.

T. Hidayati and B. F. Putra, “Implementasi Deep Learning Untuk Image Classification menggunakan Convolutional Neural Network Pada Citra Wayang ( Studi Kasus : SDN Leuwibatu 03 ),” Sci. Sacra J. Sains, Teknol. dan Masy., vol. 4, no. 1, pp. 1–7, 2024.

Published
2024-09-17
Abstract views: 663 times
Download PDF: 446 times
How to Cite
Aras, S., Yusuf, M., Ruimassa, R., Wambrauw, E. A., & Pala’langan, E. (2024). Sentiment Analysis on Shopee Product Reviews Using IndoBERT. Journal of Information Systems and Informatics, 6(3), 1616-1627. https://doi.org/10.51519/journalisi.v6i3.814
Issue
Vol 6 No 3 (2024): Journal of Information Systems and Informatics
Section
Articles

Copyright (c) 2024 Journal of Information Systems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

  1. I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
  2. I certify that all authors have approved the publication of this and there is no conflict of interest.
  3. I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
  4. I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
  6. I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
  7. If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
  8. I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)