Comparison of Service and Ease of e-Commerce User Applications Using BERT
Abstract
The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
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References
X. Liu et al., “Cyber security threats: A never-ending challenge for e-commerce,” Front. Psychol., vol. 13, no. October, hal. 1–15, 2022, doi: 10.3389/fpsyg.2022.927398.
G. S. Mahendra dan P. G. S. C. Nugraha, “Komparasi Metode AHP-SAW dan AHP-WP Pada SPK Penentuan E-Commerce Terbaik di Indonesia,” J. Sist. dan Teknol. Inf., vol. 8, no. 4, hal. 346, 2020, doi: 10.26418/justin.v8i4.42611.
A. Dwivedi, N. Panchal, A. K. Pandey, dan J. Desai, “Analysing the Paralysis: Inquiry into the Paradox of Choices in Online Apparel Shopping,” no. August, 2020.
Rabihi Awaludin, “Perancangan Aplikasi Wisata Virtual Untuk Pemulihan Ekonomi Kawasan Wisata Pantai Pangandaran di Masa Pandemi,” J. Sist. Cerdas, vol. 4, no. 2, hal. 95–103, Agu 2021, doi: 10.37396/jsc.v4i2.133.
D. Margahayu, “Tinjauan Teknologi Cerdas Pendukung Pemulihan Ekonomi UMKM Terdampak Covid-19,” J. Sist. Cerdas, vol. 4, no. 3, hal. 151–154, Des 2021, doi: 10.37396/jsc.v4i3.151.
V. Jain, B. Malviya, dan S. Arya, “An Overview of Electronic Commerce (e-Commerce),” J. Contemp. Issues Bus. Gov., vol. 27, no. 3, 2021, doi: 10.47750/cibg.2021.27.03.090.
E. P. Yudha, A. A. Rifai, dan A. S. Adela, “ANALISIS TINGKAT KEPUASAN KONSUMEN TERHADAP KUALITAS PRODUK DAN KUALITAS PELAYANAN RESTORAN CEPAT SAJI McDONALD’S,” Mimb. Agribisnis J. Pemikir. Masy. Ilm. Berwawasan Agribisnis, vol. 8, no. 2, hal. 1003, 2022, doi: 10.25157/ma.v8i2.7558.
I. N. Kabiru dan P. K. Sari, “Analisa Konten Media Sosial E-commerce Pada Instagram Menggunakan Metode Sentiment Analysis Dan Lda-based Topic Modeling (studi Kasus: Shopee Indonesia),” eProceedings Manag., vol. 6, no. 1, hal. 12–19, 2019.
R. R. Armayani, L. C. Tambunan, R. M. Siregar, N. R. Lubis, dan A. Azahra, “Analisis Peran Media Sosial Instagram Dalam Meningkatkan Penjualan Online,” J. Pendidik. Tembusai Fak. Ilmu Pendidik. Univ. Pahlawan, vol. 5, no. 3, hal. 8920–8928, 2021.
V. Larasati dan E. Oktivera, “Media Sosial Instagram Berpengaruh Terhadap Minat Beli Produk Wardah,” J. Adm. Kant., vol. 7, no. 1, hal. 31–40, 2019.
G. Chowdhury, “Natural language processing . Annual Review of This is an author-produced version of a paper published in The Annual Review of Information Science and Technology ISSN 0066-4200 . This version has been peer-reviewed , but does not,” Annu. Rev. Inf. Sci. Technol., vol. 37, hal. 51–89, 2003.
R. Cahyadi, A. Damayanti, dan D. Aryadani, “Recurrent Neural Network (RNN) dengan Long-Short Term Memory (LSTM) untuk Analisis Sentimen Data Instagram,” J. Inform. dan Komput., vol. 5, no. 1, hal. 1–9, 2020.
F. N. Hasan dan M. Dwijayanti, “Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier,” J. Linguist. Komputasional, vol. 4, no. 2, hal. 52–58, 2021, doi: https://doi.org/10.26418/jlk.v4i2.61.
W. Paulina, F. A. Bachtiar, dan A. N. Rusydi, “Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Terhadap Kertanegara Premium Guest House Menggunakan Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 4, hal. 1141–1149, 2020.
M. Wankhade, A. C. S. Rao, dan C. Kulkarni, A survey on sentiment analysis methods, applications, and challenges, vol. 55, no. 7. Springer Netherlands, 2022.
L. Mathew dan V. R. Bindu, “A Review of Natural Language Processing Techniques for Sentiment Analysis using Pre-trained Models,” Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, hal. 340–345, 2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00064.
H. Rehana, N. B. Çam, M. Basmaci, Y. He, A. Özgür2, dan J. Hur, “Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text,” arXiv Prepr. arXiv2303.17728, no. 10, hal. 1–25, 2023.
J. Devlin, M. W. Chang, K. Lee, dan K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, no. Mlm, hal. 4171–4186, 2019.
R. Mas, R. W. Panca, K. Atmaja1, dan W. Yustanti2, “Analisis Sentimen Customer Review Aplikasi Ruang Guru dengan Metode BERT (Bidirectional Encoder Representations from Transformers),” Jeisbi, vol. 02, no. 03, hal. 2021, 2021.
R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, dan M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, hal. 122–129, 2021, doi: 10.36341/rabit.v6i2.1765.
M. Singh, A. K. Jakhar, dan S. Pandey, “Sentiment analysis on the impact of coronavirus in social life using the BERT model,” Soc. Netw. Anal. Min., vol. 11, no. 1, hal. 1–11, 2021, doi: 10.1007/s13278-021-00737-z.
R. Duan, Z. Huang, Y. Zhang, X. Liu, dan Y. Dang, “Sentiment Classification Algorithm Based on the Cascade of BERT Model and Adaptive Sentiment Dictionary,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/8785413.
