<![CDATA[The KIP-K scholarship program provides crucial educational support for underprivileged students, yet its manual selection process at the University of Mataram has been plagued by inefficiency, subjectivity, and inconsistency. This study develops an integrated decision-support system combining Analytic Hierarchy Process (AHP), Skyline Query, and TOPSIS methodologies to revolutionize the selection process. The AHP method established weighted criteria, identifying poverty card ownership (23.24%) and number of family dependents (18.61%) as the most critical factors. Skyline Query processing of 500 applicants yielded 68 non-dominated candidates representing optimal poverty profiles across multiple dimensions. TOPSIS analysis then generated objective rankings, with top candidate P499 achieving an exceptional CI score of 0.872. The integrated system demonstrated remarkable consistency (CR < 0.1) and improved selection accuracy by 22% compared to traditional methods. Jaccard Distance analysis (0.0-0.9) further validated the Skyline Filter's effectiveness in maintaining top-tier candidates while optimizing mid-tier selections. This research presents a transformative approach to scholarship allocation, offering complete elimination of subjective bias, handling of large applicant pools (500 candidates) with computational efficiency, a transparent, multidimensional assessment framework. The results prove this hybrid system's superiority in identifying truly deserving recipients while processing applications at scale. The study concludes that the AHP-Skyline-TOPSIS integration establishes a new standard for equitable, data-driven scholarship distribution, with immediate applicability to other social assistance programs in higher education.]]>
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