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Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia

Research Article

Development of a Sustainable Replanting Simulation System

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  • @INPROCEEDINGS{10.4108/eai.24-10-2023.2343486,
        author={Mhd Dominique  Mendoza and Fevi  Rahmawati Suwanto and Ishaq  Matondang},
        title={Development of a Sustainable Replanting Simulation System},
        proceedings={Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia},
        publisher={EAI},
        proceedings_a={ICIESC},
        year={2024},
        month={1},
        keywords={machine learning management information system replanting simulation},
        doi={10.4108/eai.24-10-2023.2343486}
    }
    
  • Mhd Dominique Mendoza
    Fevi Rahmawati Suwanto
    Ishaq Matondang
    Year: 2024
    Development of a Sustainable Replanting Simulation System
    ICIESC
    EAI
    DOI: 10.4108/eai.24-10-2023.2343486
Mhd Dominique Mendoza1,*, Fevi Rahmawati Suwanto1, Ishaq Matondang1
  • 1: Universitas Negeri Medan
*Contact email: aenaen@unimed.ac.id

Abstract

Replanting is a crucial agricultural procedure that entails the substitution of harvested crops with fresh onesThe process entails the acquisition of diverse data sets essential for comprehending the system under consideration for modeling purposes, alongside their integration into a cohesive database. In the realm of agricultural simulation system development, the necessary data encompasses several elements such as soil conditions, historical weather data, crop production estimates, market pricing, and additional economic aspects. A simulation model was designed to include machine learning methods for the purpose of predicting agricultural yields in varying environmental circumstances. The implementation of a sustainable replanting simulation system signifies a notable advancement in the promotion of agricultural methods that prioritize sustainability. This system offers decision-makers a robust tool for the planning and management of replanting efforts by incorporating data analytics and simulation methodologies

Keywords
machine learning management information system replanting simulation
Published
2024-01-25
Publisher
EAI
http://dx.doi.org/10.4108/eai.24-10-2023.2343486
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