<![CDATA[Educational data management is essential for supporting policy planning and improving decision-making processes in the education sector. Nevertheless, collecting DAPODIK data manually often requires considerable time and may increase the possibility of human error because the information is distributed across multiple web pages. This study aims to design and assess an automated system for extracting elementary school DAPODIK data through Python-based web scraping techniques. A quantitative experimental approach was applied using Selenium WebDriver and BeautifulSoup to obtain educational data from the official DAPODIK reference website. The collected dataset involved 40 elementary schools located in Lais District, Musi Banyuasin Regency, consisting of school identity, NPSN, accreditation level, number of students, number of teachers, and school status. Data preprocessing procedures included data cleaning, standardization, type conversion, and duplicate elimination with the assistance of the Pandas library. The experimental results indicate that the proposed system achieved a 100% extraction success rate with no detected errors and completed the scraping process within 133.61 seconds. In addition, the extracted dataset showed consistent and valid numerical as well as categorical information suitable for further analytical processing. The findings demonstrate that automated web scraping can improve the speed, accuracy, and consistency of DAPODIK data collection compared with conventional manual methods. Furthermore, the developed framework has the potential to support large-scale educational data management and monitoring systems in the future.]]>
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