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The activity of admitting new students (PPDB) is an administrative process that is repeated every year. This activity is the starting point for the process of finding quality resources according to the criteria of each school. Selection is done manually, such as using spreadsheets or number processing, causing problems, including the length of the selection process. This study develops a PPDB selection system that facilitates the process of accepting new students. The development of this research uses Javascript Node JS, React JS framework, MySQL database from Xampp, and visual studio code editor. The system was built using two methods, namely Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW). AHP is used to select prospective students, while SAW is used as a way to map the majors of each prospective student. The input criteria are National Achievement Test (NUN), School Achievement Test (NUS), Academic Potential Test (TPA), entry path, and major interest. Research has succeeded in building an application that produces rankings and majors that are 100% the same as the calculation simulations carried out manually. The test was carried out with a black box test with 100% valid results. The results of the selection were then tested using the alpha test and beta test. Respondents gave responses strongly agree 83% and agree 17%, while the responses do not know/undecided, disagree, and strongly disagree each 0%.
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Stored only when both papers are in the corpus — 8 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.