A Decision Support System for Optimal Rice Cultivation Site Selection Using the Additive Ratio Assessment (ARAS) Method
Keywords:
Decision Support System, ARAS, Multi-Criteria Decision Making, Rice Cultivation, Site Selection, Sustainable AgricultureAbstract
Optimal site selection for rice cultivation is a critical factor in enhancing agricultural productivity and ensuring regional food security, particularly in areas with heterogeneous agro-climatic conditions. However, decision-making related to rice planting locations is often constrained by fragmented data utilization and the absence of systematic multi-criteria evaluation frameworks. This study proposes a data-driven Decision Support System (DSS) based on the Additive Ratio Assessment (ARAS) method to identify optimal rice cultivation locations at a provincial scale in Central Java, Indonesia. The proposed framework evaluates 35 administrative regions (regencies and cities) using five key criteria: groundwater availability, irrigation infrastructure, harvested rice area, rice production, and rainfall intensity. Secondary data were obtained from the Central Java Statistics Bureau (BPS) and processed through a structured multi-criteria decision-making (MCDM) procedure, including decision matrix construction, normalization, weighted utility calculation, and ranking. To strengthen methodological robustness, the ARAS results were systematically compared with the MOOSRA method, and the consistency of rankings was statistically validated using Spearman’s rank correlation. The results indicate that Cilacap Regency consistently achieved the highest utility score, identifying it as the most optimal location for rice cultivation, while Tegal City ranked lowest. The Spearman correlation coefficient of 0.922 demonstrates a very strong agreement between ARAS and MOOSRA rankings, confirming the reliability of the proposed approach. The novelty of this study lies in the application of an ARAS-based DSS for large-scale regional agricultural site selection, combined with cross-method validation to enhance decision reliability—an aspect rarely addressed in prior rice cultivation studies. The proposed framework provides a practical, transparent, and replicable decision-support tool for policymakers and agricultural planners, contributing to sustainable agricultural planning and evidence-based food security strategies.
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