Analysis Of E-Commerce Product With Web Scraping Technique
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Abstract
This research aims to implement a web scraping system to automatically extract product data from the e-commerce platform Bukalapak, with the goal of supporting statistical analysis at the Central Bureau Statistics (BPS) of West Java Province. The system utilizes a combination of API access and automation tools such as python, executed in the Google Colab cloud environment. Through this method, 74,796 product records were successfully collected, encompassing information such as product names, prices, categories, customer reviews, stock levels, and seller locations. The data was then processed and visualized using bar charts and histograms to analyze market trends, price distribution, and consumer behavior across regions in West Java. The results show that most products fall within affordable ranges, with certain categories like electronics and personal care dominating in volume. The scraping approach proved to be an efficient and scalable solution for acquiring real-time market data, supporting BPS in evidence-based decision-making and policy formulation.
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