Penerapan ETL dan Decision Tree untuk Klasifikasi Rating Produk Tokopedia
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Abstract
The growth of e-commerce in Indonesia has increased the volume of transaction data and customer reviews. Tokopedia, as one of the largest e-commerce platforms, generates a large amount of product and review data that can be analyzed to support decision-making. This study aims to analyze factors affecting Tokopedia product review ratings by applying the Extract, Transform, Load (ETL) process and the Decision Tree classification method. The dataset was obtained from Kaggle and includes product, seller, and customer review information. The results show that variables such as price, number of products sold, seller status, and product category influence review ratings. The Decision Tree model provides clear interpretation of decision-making patterns in determining product ratings.
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