KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming

Cucus, Ahmad and Al Fahim, Mubarak Ali and Afrig, Aminuddin and Pristyanto, Yoga and Abdulloh, Ferian Fauzi and Zafril Rizal, M. Azmi (2023) KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming. In: 2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding. International Conference on Advanced Engineering and Technologies , 13 October 2023 , Kediri, Indonesia. pp. 237-242.. ISBN 979-8-3503-0648-4 (Published)

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Abstract

One of the problems in agriculture is the difficulty of determining the correct type of plant on land with certain conditions. In this study, we tried to apply an algorithm to determine the commodity according to the conditions of the planting area. The idea is to find a match between the variables owned by the environment and the plant profile. The algorithm used in this research is KNN, which was chosen because the variable has a numeric data type to perform the matching. However, sometimes, the matching does not only use variable data types. In some cases, the available variables are string data types. In this study, the researchers tried to improve the matching method on KNN to adjust the string data type variables. At the end of this study, data visualization showed the types of plants that match the case examples from a field. It proves that the proposed method can classify string data types.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Smart farming, KNN Algorithm, Computational intelligence
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	10 Sep 2024 06:38
Last Modified:	10 Sep 2024 06:38
URI:	http://umpir.ump.edu.my/id/eprint/40255
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