Application of ROC, MAUT, and MOORA Methods in a Decision Support System for Selecting Superior Corn Seeds
DOI:
https://doi.org/10.55537/spk.v3i2.787Keywords:
MAUT, MOORA, ROC, Decision Support SystemAbstract
This study aims to highlight the importance of selecting superior corn seeds for optimal results. The primary focus of the research is to establish criteria for superior corn seeds with specifications including seed age of 120 days, moisture content of 15-20%, clean plants, a minimum of 85% dry husk population, and a germination test of 95%. Based on these criteria, "Corn Seed A" is recognized as the superior corn seed. Farmers often face difficulties in determining corn seed criteria, which affects its chemical composition and yield.
To address this issue, the study applies a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT), Multi-Objective Optimization by Ratio Analysis (MOORA), and Rank Order Centroid (ROC) weighting methods. The computerized DSS is used to analyze data, calculate criteria, and convert data into information that supports farmers' decision-making. The research results show that the MAUT, MOORA, and ROC weighting methods effectively assist in determining superior corn seeds, with "Corn Seed A" being the best choice based on the established criteria. These findings contribute significantly to the development of knowledge and scientific solutions to support optimal decision-making in the agricultural sector. The approach used in this study is expected to be widely applicable in decision-making processes in agriculture to improve seed quality and crop yield.
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