Integrasi PIPRECIA dan MACROS Dalam Penentuan Lokasi Pertanian Berkelanjutan - Irma Hakim; Asdi Asdi; Oktoni Eryanto (Bulletin of Computer Science Research, 2025)
This study develops a decision support system for selecting sustainable agricultural locations amid the challenges of climate change. Two integrated multi-criteria decision-making (MCDM) methods are employed: PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) to determine the importance weights of criteria based on expert rankings, and MACROS (Measurement Alternatives and Ranking according to the Compromise Solution), a structured method used to evaluate and rank alternatives through normalization, weighted scoring, and compromise-based ranking. Six key criteria are considered: water availability, land suitability, flood risk, market access, government support, and microclimate conditions. The integration of PIPRECIA and MACROS enables a systematic and transparent evaluation process. The results indicate that location "G" achieves the highest compromise score of 0.985, signifying its suitability as the most optimal site for sustainable agricultural development. The primary contribution of this research lies in offering a quantitative and structured approach that accommodates environmental uncertainties while enhancing decision-making transparency. By integrating expert judgment with computational assessment, this model supports data-driven decision-making in the planning of agricultural development. These findings are expected to provide strategic insights for policymakers in formulating adaptive agricultural policies, strengthening food security, and improving farmer welfare through accurate and sustainable location selection.