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Abstract

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term dynamics. Official data from the ASEAN Food Security Information System (AFSIS) is analysed using a macro-level comparative approach. AHP is implemented in Python to efficiently perform matrix normalisation, pairwise comparisons, eigenvalue calculations, and final score synthesis, thereby improving accuracy, scalability, and reproducibility while reducing human error. Results show that Indonesia (0.678) and Vietnam (0.654) consistently rank among the highest-performing countries, highlighting their strategic role in the regional stability of rice production. This study contributes a novel integration of AHP and Python for regional agricultural performance modelling, providing methodological advancements and practical insights. The findings support evidence-based policymaking, cross-country collaboration, and resource planning, promoting food security and agricultural resilience across ASEAN.

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