Analisis Sentimen Ulasan Pengguna E-commerce di Google Play Store Menggunakan Metode IndoBERT
- Kireyna Cindy Pradhisa Universitas Islam Indonesia, Yogyakarta, Indonesia
-
Rohmatul Fajriyah
*
Universitas Islam Indonesia, Yogyakarta,
Indonesia
(*) Corresponding Author
Abstract
In today's booming digital age, the internet has created global connectivity that links individuals around the world in a global community. Through synergistic collaboration between commerce and information technology, the term e-commerce was born. The analysis of e-commerce product quality is conducted to understand the public's perception of the products offered through the e-commerce platform, which can be accessed through the Google Play Store. The purpose of this research is to find out the sentiment trends of e-commerce users and to know how accurate IndoBERT is in classifying sentiment. By doing so, the founders and managers of both platforms can gain a better understanding of their users' needs and preferences. This information will serve as a foundation for improving service quality and user experience, thereby increasing customer satisfaction and strengthening competitive position in the e-commerce market. In this study, a sentiment analysis of the products of e-commerce platforms Shopee and Bukalapak is conducted, using the NLP-based IndoBERT (Natural Language Processing) model, which classifies the sentiment of user reviews into three categories: negative, neutral, and positive. The review data was taken from Google Play Store with scrapping technique and involved 5000 Shopee and Bukalapak review data with 2500 data each in 2023. The accuracy obtained is 89.84% on Shopee review data and 88.12% on Bukalapak review data. This shows that the model is able to effectively identify sentiment in each user review. Furthermore, another result obtained is that the sentiment of Shopee and Bukalapak e-commerce application users tends to be positive. Therefore, it can be said that the products offered and services carried out by e-commerce platforms Shopee and Bukalapak in 2023 get a good response from the public, which shows that both platforms are trusted and favored by the public.
Downloads
References
D. Hendarsyah, “E-Commerce Di Era Industri 4.0 Dan Society 5.0,” IQTISHADUNA J. Ilm. Ekon. Kita, vol. 8, no. 2, pp. 171–184, 2019, doi: 10.46367/iqtishaduna.v8i2.170.
A. Alwendi, “Penerapan E-Commerce Dalam Meningkatkan,” Manaj. Bisnis, vol. 17, no. 3, pp. 317–325, 2020, [Online]. Available: http://journal.undiknas.ac.id/index.php/magister-manajemen/
N. Ramadhani, D. R. Nandani, F. Arrizki, and Nurbaiti, “Peran Digitalisasi Teknologi Informasi Dalam Proses Pengembembangan E-Commerce di Era 4.0 dan Society 5.0,” J. Ekon. Syariah, pp. 225–232, 2023.
G. Pratama, “Analisis Transaksi Jual Beli online Melalui Website Marketplace Shopee Menurut Konsep Bisnis di Masa Pandemic Covid 19,” Ecopreneur J. Progr. Stud. Ekon. Syariah, vol. 1, no. 2, p. 21, 2020, doi: 10.47453/ecopreneur.v1i2.130.
R. R. M. Salim, A. F. Azzahra, A. S. Sembiring, and Y. M. Saragih, “Evaluasi Usability Website Shopee.co.id dan Bukalapak.com dengan Menggunakan Metode Webuse,” Remik, vol. 7, no. 2, pp. 946–954, 2023, doi: 10.33395/remik.v7i2.12288.
M. Ikhsan Thohir, S. Indra Mulyana, and F. Sembiring, “Analisis Sentimen Aplikasi Dompet Digital Pada Google Playstore Dengan Metode SAW,” J. Sist. Inf. dan Teknol. Informasi), vol. 5, no. 2, pp. 202–213, 2023.
A. S. P. Braja and A. Kodar, “Implementasi Fine-Tuning BERT untuk Analisis Sentimen terhadap Review Aplikasi PUBG Mobile di Google Play Store,” J I M P - J. Inform. Merdeka Pasuruan, vol. 7, no. 3, p. 120, 2023, doi: 10.51213/jimp.v7i3.779.
Nelly Sofi, Tri Sulistyorini, and Muhammad Nazaruddin, “Analisis Sentimen Masyarakat Pengguna Media Sosial Twitter Terhadap Motogp Mandalika Lombok Menggunakan Metode Bidirectional Encoder Representation From Transformers (BERT),” J. Informasi, Sains dan Teknol., vol. 6, no. 1, pp. 120–130, 2023, doi: 10.55606/isaintek.v6i1.103.
A. Chiorrini, C. Diamantini, A. Mircoli, and D. Potena, “Emotion and sentiment analysis of tweets using BERT,” CEUR Workshop Proc., vol. 2841, no. March, 2021.
N. Husin, “Komparasi Algoritma Random Forest, Naïve Bayes, dan Bert Untuk Multi-Class Classification Pada Artikel Cable News Network (CNN),” J. Esensi Infokom J. Esensi Sist. Inf. dan Sist. Komput., vol. 7, no. 1, pp. 75–84, 2023, doi: 10.55886/infokom.v7i1.608.
B. Rahmatullah, Sentiment Analysis Pelaksanaan Work From Home di Indonesia pada Masa Pandemi COVID-19 Menggunakan IndoBERT. 2021. [Online]. Available: https://repository.its.ac.id/91493/1/06111640000078-Undergraduate_Thesis.pdf
D. Fimoza, “Analisis Sentimen Terhadap Film Indonesia Dengan Pendekatan BERT,” Expert Syst. Appl., vol. 118, no. 1, pp. 272–299, 2019, [Online]. Available: http://repositori.usu.ac.id/handle/123456789/30445%0Ahttps://linkinghub.elsevier.com/retrieve/pii/S0957417418306456
R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, and M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, pp. 122–129, 2021, doi: 10.36341/rabit.v6i2.1765.
S. Satriajati, S. B. Panuntun, and S. Pramana, “Implementasi Web Scraping Dalam Pengumpulan Berita Kriminal Pada Masa Pandemi Covid-19,” Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 300–308, 2021, doi: 10.34123/semnasoffstat.v2020i1.578.
F. Alghifari and D. Juardi, “Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes,” J. Ilm. Inform., vol. 9, no. 02, pp. 75–81, 2021, doi: 10.33884/jif.v9i02.3755.
H. Tuhuteru and A. Iriani, “Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier,” J. Inform. J. Pengemb. IT, vol. 3, no. 3, pp. 394–401, 2018, doi: 10.30591/jpit.v3i3.977.
S. Diantika, “Penerapan Teknik Random Oversampling Untuk Mengatasi Imbalance Class Dalam Klasifikasi Website Phishing Menggunakan Algoritma Lightgbm,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 19–25, 2023, doi: 10.36040/jati.v7i1.6006.
S. Imron, E. I. Setiawan, and J. Santoso, “Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN,” J. Intell. Syst. Comput., vol. 5, no. 1, pp. 10–16, 2023, doi: 10.52985/insyst.v5i1.267.
K. Sentimen, U. Aplikasi, D. I. P. Store, and M. M. K. Neighbor, “DI PLAY STORE,” 2024.
M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine,” J. Teknol. dan Komun. Pemerintah., vol. 4, no. 1, pp. 1–13, 2022, doi: 10.33701/jtkp.v4i1.2701.
M. W. Prayuda and A. A. Permana, “Penerapan Metode Lexicon Based untuk Menganalisis Sentimen Terhadap Mudik Lebaran,” J. Minfo Polgan, vol. 11, no. September, pp. 137–143, 2022, [Online]. Available: https://www.jurnal.polgan.ac.id/index.php/jmp/article/view/12348%0Ahttps://www.jurnal.polgan.ac.id/index.php/jmp/article/download/12348/1550
P. A. Sumitro, Rasiban, D. I. Mulyana, and W. Saputro, “Analisis Sentimen Terhadap Vaksin Covid-19 di Indonesia pada Twitter Menggunakan Metode Lexicon Based,” J-ICOM - J. Inform. dan Teknol. Komput., vol. 2, no. 2, pp. 50–56, 2021, doi: 10.33059/j-icom.v2i2.4009.
J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Naacl-Hlt 2019, no. Mlm, pp. 4171–4186, 2018, [Online]. Available: https://aclanthology.org/N19-1423.pdf
F. Rayyan, Pengembangan chatbot untuk aplikasi online chat telegram dengan pendekatan klasifikasi emosi pada teks menggunakan metode indobert-lite. 2022.
T. Iskandar Zulkarnain Maulana Putra, A. Farhan Bukhori, dan Ilmu Pengetahuan Alam, and U. Gadjah Mada, “Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia (Classification Model Based on Multiclass Classification with a Combination of Indobert Embedding and Long ,” J. Ilmu Siber dan Teknol. Digit., vol. 1, no. 1, pp. 1–28, 2022, [Online]. Available: https://doi.org/10.35912/jisted.v1i1.1509
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Ulasan Pengguna E-commerce di Google Play Store Menggunakan Metode IndoBERT
Pages: 92−104
Copyright (c) 2024 Kireyna Cindy Pradhisa, Rohmatul Fajriyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
