<![CDATA[The manual monitoring process for students receiving the Indonesia Smart College Card (KIP-K) at Universitas Kebangsaan Republik Indonesia is currently considered inefficient, lacking objectivity, and without a centralized system or early warning mechanism. These fundamental shortcomings pose a risk to the academic performance of students who go unmonitored, thereby undermining the program’s goal of maintaining academic quality.This study aims to address these issues by designing and implementing an objective assessment model within a decision support system. The method employed is Simple Additive Weighting (SAW), specifically enhanced by a multi-indicator approach to improve objectivity. This approach systematically quantifies qualitative criteria—such as organizational involvement—into measurable numerical scores, alongside other indicators such as GPA and attendance.The criteria and preference weights used in the model were validated through interviews with relevant stakeholders and a questionnaire distributed to 50 respondents. The result of this research is a functional system capable of automating the assessment process, accurately generating student performance rankings, and providing proactive email notifications as an early warning mechanism.It is concluded that the implementation of the SAW method, reinforced by a multi-indicator approach, is an effective solution. The application of this method has proven to enhance accuracy, transparency, and efficiency in data-driven monitoring of KIP-K students, while also serving as a reliable and transparent decision-making tool for student affairs administrators. Jika Anda ingin versi yang lebih ringkas untuk abstrak jurnal atau versi yang lebih formal untuk laporan kebijakan, saya siap bantu menyusunnya.]]>
Copyrights © 2025
