Multivariate Analysis for Formulating Data-Driven Recruitment Strategies in the Informatics Engineering Study Program, UNIKS
Abstract
This research aims to formulate an effective and data-driven recruitment strategy for the Informatics Engineering Study Program (TI) at Kuantan Singingi Islamic University (UNIKS) through a comprehensive analysis of student data. Expanding upon previous descriptive studies, this research applies a quantitative methodology with a case study approach that integrates cluster analysis, correlation, and association testing (Chi-Squared). Historical student data from 2019 to 2025 were processed and visualized using R Studio. The results confirm a strong positive correlation between the year and the number of new students. Furthermore, cluster analysis successfully grouped districts (kabupaten) and sub-districts (kecamatan) into clusters based on applicant potential, indicating an uneven geographical distribution. Moreover, the Chi-Squared test revealed a significant relationship between the number of students and demographic variables such as admission track and parental income. These findings provide strategic insights to focus promotional efforts on geographical areas with the highest potential and to target audiences based on socio-economic characteristics. Thus, R Studio proves to be a powerful tool to support data-driven strategic decision-making in the academic environment.
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