ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY

Aris Gunaryati; Teddy Mantoro; Septi Andryana; Benrahman; Mohammad Iwan Wahyuddin

doi:10.33480/jitk.v11i2.6788

Authors

Aris Gunaryati Universitas Nasional
Teddy Mantoro Nusa Putra University
Septi Andryana Universitas Nasional
Benrahman Universitas Nasional
Mohammad Iwan Wahyuddin Universitas Nasional

DOI:

https://doi.org/10.33480/jitk.v11i2.6788

Keywords:

AHP, coffee production, consistency ratio, MCDM, multiple linear regression

Abstract

The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors

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