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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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2023
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| 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 |
| _version_ | 1811138302755995648 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-09-25T03:48:02Z |
| format | Conference or Workshop Item |
| id | UMPir40255 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:48:02Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir402552024-09-10T06:38:26Z 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 |
| 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 |
| title | 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_short | KNN Algorithm to Determine Optimum Agricultural Commodities in Smart Farming |
| title_sort | knn algorithm to determine optimum agricultural commodities in smart farming |
| topic | QA75 Electronic computers. Computer science |
| 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 |
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