Rekomendasi Produk E-commerce Berbasis Klasifikasi Ulasan Menggunakan Ensemble Random Forest dan Teknik Boosting
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Abstract
The increasing popularity of e-commerce poses a new challenge to provide customers with a more personalized and effective shopping experience. In situations like this, product recommendations are very important to increase consumer satisfaction and increase sales. Using Random Forest and Boosting ensemble techniques, this research introduces a method for e-commerce product recommendation based on user review analysis. The Aim is to test the Random Forest algorithm with several boosting techniques for ensemble learning. The results show that the Random Forest method combined with the Xgboost technique can provide product recommendations that are 87.25% more accurate and relevant than other boosting techniques. In precision analysis, Random Forest-XGBoost achieved a higher accuracy of 90% compared to other boosting techniques. Additionally, the combined use of Boosting and Random Forest techniques improves the model's performance in handling complexity and variation in e-commerce product reviews.
References
D. M. Alghazzawi, A. G. A. Alquraishee, S. K. Badri, and S. H. Hasan, “ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review,” Sustainability (Switzerland), vol. 15, no. 9, May 2023, doi: 10.3390/su15097076.
Stephenie, B. Warsito, and A. Prahutama, “Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method,” in E3S Web of Conferences, EDP Sciences, Nov. 2020. doi: 10.1051/e3sconf/202020216006.
N. Verma, S. Sood, K. Kumari, and N. Kumari, “The Impact of Product Reviews on E-Commerce Performance: A Comprehensive Review,” Int J Res Appl Sci Eng Technol, vol. 11, no. 7, pp. 1258–1264, Jul. 2023, doi: 10.22214/ijraset.2023.54849.
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.
J. Mutinda, W. Mwangi, and G. Okeyo, “Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network,” Applied Sciences (Switzerland), vol. 13, no. 3, Feb. 2023, doi: 10.3390/app13031445.
A. Onan, “Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks,” in Concurrency and Computation: Practice and Experience, John Wiley and Sons Ltd, Dec. 2021. doi: 10.1002/cpe.5909.
A. A. Farisi, Y. Sibaroni, and S. Al Faraby, “Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier,” in Journal of Physics: Conference Series, Institute of Physics Publishing, May 2019. doi: 10.1088/1742-6596/1192/1/012024.
Q.-F. Li and Z.-M. Song, “High-performance concrete strength prediction based on ensemble learning,” Constr Build Mater, vol. 324, p. 126694, Mar. 2022, doi: 10.1016/j.conbuildmat.2022.126694.
Z. Shahbazi, D. Hazra, S. Park, and Y. C. Byun, “Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches,” Symmetry (Basel), vol. 12, no. 9, p. 1566, Sep. 2020, doi: 10.3390/sym12091566.
I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10. Institute of Electrical and Electronics Engineers Inc., pp. 99129–99149, 2022. doi: 10.1109/ACCESS.2022.3207287.
Z. Li and N. Zhang, “Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/5227829.
P. D. Caie, N. Dimitriou, and O. Arandjelović, “Precision medicine in digital pathology via image analysis and machine learning,” in Artificial Intelligence and Deep Learning in Pathology, Elsevier, 2021, pp. 149–173. doi: 10.1016/B978-0-323-67538-3.00008-7.
S. Hong and H. S. Lynn, “Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction,” BMC Med Res Methodol, vol. 20, no. 1, p. 199, Dec. 2020, doi: 10.1186/s12874-020-01080-1.
M. Alojail and S. Bhatia, “A Novel Technique for Behavioral Analytics Using Ensemble Learning Algorithms in E-Commerce,” IEEE Access, vol. 8, pp. 150072–150080, 2020, doi: 10.1109/ACCESS.2020.3016419.
J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, “Boosting methods for multi-class imbalanced data classification: an experimental review,” J Big Data, vol. 7, no. 1, Dec. 2020, doi: 10.1186/s40537-020-00349-y.
S. Ben Jabeur, S. Mefteh-Wali, and J. L. Viviani, “Forecasting gold price with the XGBoost algorithm and SHAP interaction values,” Ann Oper Res, Mar. 2021, doi: 10.1007/s10479-021-04187-w.
“Retracted: Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm,” Advances in Multimedia, vol. 2023, pp. 1–1, Dec. 2023, doi: 10.1155/2023/9805382.
K. Singh, P. M. Booma, and U. Eaganathan, “E-Commerce System for Sale Prediction Using Machine Learning Technique,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Dec. 2020. doi: 10.1088/1742-6596/1712/1/012042.
A. Y. A Bani Ahmad, T. Naidu Gongada, G. Shrivastava, R. Singh Gabbi, S. Islam, and K. Nagaraju, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING E-Commerce Trend Analysis and Management for Industry 5.0 using User Data Analysis.” [Online]. Available: www.ijisae.org
J. Kubrusly, A. L. Neves, and T. L. Marques, “A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods,” Open J Stat, vol. 12, no. 03, pp. 357–372, 2022, doi: 10.4236/ojs.2022.123023.
R. Jhangiani, D. Bein, and A. Verma, “Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions,” 2019.
R. M. Aziz, M. F. Baluch, S. Patel, and A. H. Ganie, “LGBM: a machine learning approach for Ethereum fraud detection,” International Journal of Information Technology (Singapore), vol. 14, no. 7, pp. 3321–3331, Dec. 2022, doi: 10.1007/s41870-022-00864-6.
M. J. Hossain, D. Das Joy, S. Das, and R. Mustafa, “Sentiment Analysis on Reviews of E-commerce Sites Using Machine Learning Algorithms,” in 2022 International Conference on Innovations in Science, Engineering and Technology, ICISET 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 522–527. doi: 10.1109/ICISET54810.2022.9775846.
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