Implementation of SerpApi-Based Google Scholar Monitoring System for Faculty Publication Evaluation
DOI:
https://doi.org/10.32664/j-intech.v14i02.2298Kata Kunci:
Accreditation, Django, Evaluation, Faculty Performance Evaluation, Google Scholar, Serp APIAbstrak
Evaluating faculty performance based on scientific publication metrics is a crucial tool in higher education quality assurance and accreditation reporting. However, conventional web scraping techniques based on HTML DOM parsing are highly vulnerable to layout changes and anti-bot blocking on Google Scholar. This study aims to develop a web-based Faculty Publication Monitoring System using the Django framework and SerpApi as a stable data extraction solution, following the Waterfall Software Development Life Cycle (SDLC) model. The results demonstrate that the API integration successfully bypasses anti-bot mechanisms with a 100% data extraction accuracy rate within the validated sample, validated through a cross-referencing protocol involving manual checks of 10 randomly sampled faculty profiles against the live Google Scholar database. Furthermore, mass data synchronization for 63 active faculty profiles was securely completed in approximately 180 seconds, while negative testing confirmed successful data normalization of null values. In conclusion, the system enhances institutional administrative efficiency in providing valid publication datasets to support institutional accreditation needs, delivering an estimated time-reduction of over 99%. Given the performance and stability delivered by this architecture, it can be adopted by other institutions facing similar challenges in automated bibliometric monitoring.
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