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KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming

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...

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Main Authors: Cucus, Ahmad, Al Fahim, Mubarak Ali, Afrig, Aminuddin, Pristyanto, Yoga, Abdulloh, Ferian Fauzi, Zafril Rizal, M. Azmi
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/40255/1/KNN%20Algorithm%20to%20Determine%20Optimum%20Agricultural%20Commodities%20in%20Smart%20Farming%20%28Intro%29.pdf
http://umpir.ump.edu.my/id/eprint/40255/13/KNN_Algorithm_to_Determine_Optimum_Agricultural_Commodities_in_Smart_Farming.pdf
http://umpir.ump.edu.my/id/eprint/40255/
https://doi.org/10.1109/ICONNIC59854.2023.10467639
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spelling my.ump.umpir.402552024-09-10T06:38:26Z http://umpir.ump.edu.my/id/eprint/40255/ KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming Cucus, Ahmad Al Fahim, Mubarak Ali Afrig, Aminuddin Pristyanto, Yoga Abdulloh, Ferian Fauzi Zafril Rizal, M. Azmi QA75 Electronic computers. Computer science 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. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40255/1/KNN%20Algorithm%20to%20Determine%20Optimum%20Agricultural%20Commodities%20in%20Smart%20Farming%20%28Intro%29.pdf pdf en http://umpir.ump.edu.my/id/eprint/40255/13/KNN_Algorithm_to_Determine_Optimum_Agricultural_Commodities_in_Smart_Farming.pdf 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) https://doi.org/10.1109/ICONNIC59854.2023.10467639
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Cucus, Ahmad
Al Fahim, Mubarak Ali
Afrig, Aminuddin
Pristyanto, Yoga
Abdulloh, Ferian Fauzi
Zafril Rizal, M. Azmi
KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
description 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.
format Conference or Workshop Item
author Cucus, Ahmad
Al Fahim, Mubarak Ali
Afrig, Aminuddin
Pristyanto, Yoga
Abdulloh, Ferian Fauzi
Zafril Rizal, M. Azmi
author_facet Cucus, Ahmad
Al Fahim, Mubarak Ali
Afrig, Aminuddin
Pristyanto, Yoga
Abdulloh, Ferian Fauzi
Zafril Rizal, M. Azmi
author_sort Cucus, Ahmad
title KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
title_short KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
title_full KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
title_fullStr KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
title_full_unstemmed KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming
title_sort knn algorithm to determine optimum agricultural commodities in smart farming
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40255/1/KNN%20Algorithm%20to%20Determine%20Optimum%20Agricultural%20Commodities%20in%20Smart%20Farming%20%28Intro%29.pdf
http://umpir.ump.edu.my/id/eprint/40255/13/KNN_Algorithm_to_Determine_Optimum_Agricultural_Commodities_in_Smart_Farming.pdf
http://umpir.ump.edu.my/id/eprint/40255/
https://doi.org/10.1109/ICONNIC59854.2023.10467639
_version_ 1822924626243616768
score 13.232492

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