Optimizing New Student Admissions

This study aims to analyze the role of Business Intelligence (BI) in improving the efficiency and quality of decisions in the New Student Admission (PMB) process. The objectives of the research include an in-depth understanding of the operational challenges faced by educational institutions, as well as efforts to utilize BI to optimize PMB data management. The research method involves the implementation of BI in the process of collecting and analyzing PMB data. Predictive analytics are used to predict the academic performance of prospective students, allowing for smarter selection. The results of the study show that BI can improve operational efficiency by accelerating the selection process and providing a more solid basis for decision-making. This study underscores the importance of implementing BI in improving the quality of decision-making at the PMB level. The implementation of this technology not only helps the institution improve operational efficiency, but also provides a competitive advantage through a better understanding of the profile of prospective students.

Authors

Publication Details

Journal
Applied Computer Science and Software Engineering
Published
2024-10-12
DOI
https://doi.org/10.63254/acsse.v1i1.17
Primary Topic
Medical Education and Admissions
Type
article
Field-Weighted Citation Impact
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article

Optimizing New Student Admissions

Nur Hamid, Zaenal Asikin
Applied Computer Science and Software Engineering
Medical Education and Admissions
article

Optimizing New Student Admissions

Nur Hamid, Zaenal Asikin
article en

Abstract

This study aims to analyze the role of Business Intelligence (BI) in improving the efficiency and quality of decisions in the New Student Admission (PMB) process. The objectives of the research include an in-depth understanding of the operational challenges faced by educational institutions, as well as efforts to utilize BI to optimize PMB data management. The research method involves the implementation of BI in the process of collecting and analyzing PMB data. Predictive analytics are used to predict the academic performance of prospective students, allowing for smarter selection. The results of the study show that BI can improve operational efficiency by accelerating the selection process and providing a more solid basis for decision-making. This study underscores the importance of implementing BI in improving the quality of decision-making at the PMB level. The implementation of this technology not only helps the institution improve operational efficiency, but also provides a competitive advantage through a better understanding of the profile of prospective students.

Applied Computer Science and Software EngineeringVol. 1(1)
Openalex Percentile: Top 60%
Medical Education and Admissions
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