Pembelajaran Ensemble untuk Klasifikasi Ulasan Pelanggan E-commerce Menggunakan Teknik Boosting
Abstract views: 131
,
PDF downloads: 103
Abstract
Technological developments have developed rapidly and impact changing behavior in daily activities. Now, selling and buying activities are carried out in e-commerce services. The increase in e-commerce users is the main factor in improving the quality of e-commerce services. One of the factors to improve the quality of e-commerce services is customer reviews. Customer reviews are useful for shop owners to find out whether the product offered has positive or negative reviews. The large number of customer reviews is the main factor in the difficulty of shop owners in classifying customer reviews. This study proposes classifying customer reviews using ensemble learning with boosting techniques such as XGBoost, AdaBoost, Gradient Boosting, and LightGBM. The use of an ensemble with a boosting technique aims to improve the algorithm’s performance. In a test scenario apply majority voting to produce the best performance from each algorithm. The result shows that the XGBoost algorithm produces higher accuracy than other techniques are 92.30%. On the analysis of matric evaluation of precision, recall, and F1-Score, XGBoost produces higher true positive values than other techniques such as AdaBoost, Gradient Boosting, and Light GBM
References
Arif Indrawan Putra and Anita Diana, “Perancangan E-Commerce dengan Business Model Canvas untuk Peningkatan Penjualan pada Toko Parfum,” Jurnal Telematika, vol. 15, no. 1, 2020.
H. Al Mashalah, E. Hassini, A. Gunasekaran, and D. Bhatt (Mishra), “The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework,” Transp Res E Logist Transp Rev, vol. 165, p. 102837, Sep. 2022, doi: 10.1016/j.tre.2022.102837.
M. Oase Ansharullah, W. Agustin, L. Lusiana, J. Junadhi, S. Erlinda, and F. Zoromi, “Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method,” JAIA-Journal Of Artificial Intelligence And Applications, vol. 2, no. 2, pp. 15–22, 2022.
L. Donati, E. Iotti, G. Mordonini, and A. Prati, “Fashion Product Classification through Deep Learning and Computer Vision,” Applied Sciences, vol. 9, no. 7, p. 1385, Apr. 2019, doi: 10.3390/app9071385.
R. E. Bawack, S. F. Wamba, K. D. A. Carillo, and S. Akter, “Artificial intelligence in E-Commerce: a bibliometric study and literature review,” Electronic Markets, vol. 32, no. 1, pp. 297–338, Mar. 2022, doi: 10.1007/s12525-022-00537-z.
A. Patra, V. Vivek, B. R. Shambhavi, K. Sindhu, and S. Balaji, “Product Classification in E-Commerce Sites,” in Advanced Computing and Intelligent Engineering, 2021, pp. 485–495. doi: 10.1007/978-981-33-4299-6_40.
L. Tan, M. Y. Li, and S. Kok, “E-Commerce Product Categorization via Machine Translation,” ACM Trans Manag Inf Syst, vol. 11, no. 3, pp. 1–14, Sep. 2020, doi: 10.1145/3382189.
A. Noor and M. Islam, “Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms,” in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2019, pp. 1–6. doi: 10.1109/ICCCNT45670.2019.8944436.
Stephenie, B. Warsito, and A. Prahutama, “Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method,” E3S Web of Conferences, vol. 202, p. 16006, Nov. 2020, doi: 10.1051/e3sconf/202020216006.
A. H. Hasugian, M. Fakhriza, and D. Zukhoiriyah, “Analisis Sentimen Pada Review Pengguna E-Commerce Menggunakan Algoritma Naïve Bayes,” J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD), vol. 6, no. 1, p. 98, Jan. 2023, doi: 10.53513/jsk.v6i1.7400.
D. L. Rianti, Y. Umaidah, and A. Voutama, “Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 6, no. 1, p. 98, Aug. 2021, doi: 10.30998/string.v6i1.9993.
I. S. Milal, M. H. M. Hasanudin, M. A. Nur Azhari, R. A. Nugraha, N. Agustina, and S. E. Damayanti, “KLASIFIKASI TEKS REVIEW PADA E-COMMERCE TOKOPEDIA MENGGUNAKAN ALGORITMA SVM,” Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika, vol. 5, no. 1, pp. 34–45, Jun. 2023, doi: 10.53580/naratif.v5i1.191.
N. Istiqamah and M. Rijal, “Klasifikasi Ulasan Konsumen Menggunakan Random Forest dan SMOTE,” Journal of System and Computer Engineering (JSCE), vol. 5, no. 1, pp. 66–77, Jan. 2024, doi: 10.61628/jsce.v5i1.1061.
I. R. Hendrawan, E. Utami, and A. D. Hartanto, “Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, pp. 143–149, Jun. 2022, doi: 10.29408/edumatic.v6i1.5613.
R. V. A. Ogutu, R. M. Rimiru, and C. Otieno, “Target Sentiment Analysis Ensemble for Product Review Classification,” Journal of Information Technology Research, vol. 15, no. 1, pp. 1–13, Aug. 2022, doi: 10.4018/JITR.299382.
S. N. Singh and T. Sarraf, “Sentiment Analysis of a Product based on User Reviews using Random Forests Algorithm,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, Jan. 2020, pp. 112–116. doi: 10.1109/Confluence47617.2020.9058128.
W. Zhao, “Classification of Customer Reviews on E-commerce Platforms Based on Naive Bayesian Algorithm and Support Vector Machine,” J Phys Conf Ser, vol. 1678, no. 1, p. 012081, Nov. 2020, doi: 10.1088/1742-6596/1678/1/012081.
N. L. Putri, B. Warsito, and B. Surarso, “Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 65–74, Feb. 2024, doi: 10.25126/jtiik.20241117376.
M. Fayaz, A. Khan, J. U. Rahman, A. Alharbi, M. I. Uddin, and B. Alouffi, “Ensemble machine learning model for classification of spam product reviews,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/8857570.
Md. M. R. Mamun, O. Sharif, and M. M. Hoque, “Classification of Textual Sentiment Using Ensemble Technique,” SN Comput Sci, vol. 3, no. 1, p. 49, Jan. 2022, doi: 10.1007/s42979-021-00922-z.
I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.
J. R. Bertini Junior and M. do C. Nicoletti, “An iterative boosting-based ensemble for streaming data classification,” Information Fusion, vol. 45, pp. 66–78, Jan. 2019, doi: 10.1016/j.inffus.2018.01.003.
Copyright (c) 2024 Matius Rama Hadi Suryanto, Danang Wahyu Utomo
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 a Creative Commons Attribution License that allows others to share the work with an acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).
