Detecting Fake Reviews in E-Commerce: A Case Study on Shopee Using Support Vector Machine and Random Forest
DOI:
https://doi.org/10.30871/jaic.v9i3.9514Keywords:
Fake Review Detection, NLP, Shopee, Random Forest, SVMAbstract
The increasing popularity of online shopping, particularly on platforms such as Shopee, has made product reviews a significant factor influencing consumer purchasing decisions. However, the presence of fake reviews generated by non-human agents undermines consumer trust and affects platform credibility. This study aims to detect fake reviews on Shopee by applying a text classification approach using Random Forest and Support Vector Machine (SVM) algorithms. A dataset consisting of 3,686 Shopee product reviews was collected and underwent preprocessing steps including data cleaning, normalization, tokenization, and TF-IDF weighting. Review labeling was performed automatically through the Latent Dirichlet Allocation (LDA) method, categorizing reviews into Original (OR) and Computer-Generated (CG). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the SVM algorithm achieved the highest accuracy at 88.84%, outperforming Random Forest which obtained 80.39%. These findings highlight the effectiveness of SVM in handling high-dimensional text data for fake review detection. The study contributes to the application of automated topic modeling (LDA) for labeling e-commerce reviews in the Indonesian context and opens opportunities for further enhancement using larger datasets and deep learning-based models to improve classification accuracy and scalability.
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