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All Journal Journal of Mathematics and Scientific Computing With Applications
Janurianty, Intan
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Mathematics

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ANALYSIS OF THE BEST FERTILIZER SELECTION FOR CORN PLANTS USING THE MULTI-ATTRIBUTE UTILITY THEORY METHOD Sahrudin; Andini, Dwi; Malakiano Ritonga, Sandrina; Janurianty, Intan; Widya, Ade
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 1 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i1.980

Abstract

Corn (Zea mays L.) is one of the most important food crops in the world, serving as a staple food for many countries, as well as a primary source of livestock feed and an industrial raw material. This research aims to determine the best fertilizer for corn plants (Zea mays L.) by applying the Multi-Attribute Utility Theory (MAUT) method, a systematic approach to multi-criteria decision making. This research was conducted in Rundeng District, Subulussalam City, Aceh, with three alternative fertilizers analyzed, namely NPK Mutiara, Urea, and Phonska. The criteria used include price, nutritional efficiency, and availability of fertilizer on the market, with weights for each criterion of 0.3, 0.5, and 0.2. The research results show that Phonska fertilizer has the highest global utility value of 0.895, making it the best choice based on the specified criteria. Phonska stands out for its optimal balance between affordable price, high nutritional efficiency and good availability on the market. Urea is in second place with a global utility value of 0.856, superior in terms of cheaper price, but lower efficiency than Phonska. Mutiara NPK, despite having the highest nutritional efficiency, only obtained a global utility value of 0.807 due to its higher price and lower availability. This research provides data-based guidance for farmers to choose the most suitable fertilizer, which is expected to increase corn crop productivity, reduce production costs, and support agricultural sustainability. By using the MAUT method, this research proves that a data-based approach can help make more rational decisions in the agricultural sector.
Co-Authors Andini, Dwi Malakiano Ritonga, Sandrina Sahrudin widya, Ade