Improving Data Completeness in SINTA Publication Scraping Using an Iterative Method - Muhammad Arfah Asis; St. Hajrah Mansyur; Nia Kurniati (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2026)
The structure of publication data on lecturer profiles in SINTA, particularly those indexed by SCOPUS, often results in data duplication and missing records. This issue arises because articles are distributed by year across multiple pages, making standard single-pass scraping methods unable to guarantee data completeness. This study aims to develop and evaluate the effectiveness of an iterative scraping method in improving the accuracy of publication data retrieval from SINTA. The proposed method involves a series of ten experimental trials, in which the results of single-pass scraping are compared with those of iterative scraping. The evaluated parameters include the level of data completeness and the number of iterations required to achieve optimal results. The findings indicate that single-pass scraping captures only an average of 70.7% of publications in the first iteration, with frequent occurrences of duplicated and missing data. In contrast, the iterative scraping method consistently achieves 100% publication retrieval across all trials, although it requires a varying number of iterations ranging from four to eleven. Therefore, it can be concluded that iterative scraping is a more reliable approach for ensuring the completeness and accuracy of publication data. Although this approach demands greater computational resources than standard methods, it is well suited for large-scale bibliometric studies, institutional evaluations, and more comprehensive monitoring of research trends.