Penerapan Metode K-Medoid Dalam Pengelompokkan Data Pertanian Di Dinas Tanaman Pangan Hortikultura Dan Perkebunan Provinsi Bengkulu

The Department of Agriculture, Food Crops, Horticulture and Plantations of Bengkulu Province has collected data on agricultural commodities in each Regency/city in Bengkulu Province which are recapitulated annually, but at the Department of Agriculture, Food Crops and Horticulture and Plantations of Bengkulu Province there is no system that can be used to classify agricultural yield data for each of these various commodities so that it is difficult to obtain information on potential harvest-producing agricultural areas in Bengkulu Province for each commodity. Therefore, a system is needed that can help provide information on the results of grouping agricultural data for each commodity so that it can be known which regency/ cities are categorized as potential crop producers in Bengkulu Province based on harvest area (Ha), production (Ton) and productivity (Ku / Ha). The implementation of K-Medoid Method in classifying agricultural data at the Department of Food Crops, Horticulture and Plantation of Bengkulu Province can provide information on the results of grouping data on agricultural areas that have potential crop yields for the last 1 year in Bengkulu Province, and can be used as a reference in providing assistance from the Department of Food Crops, Horticulture and Plantation of Bengkulu Province. From the results of clustering using the K-Medoid Method, it was found that Rejang Lebong Regency area had high yield potential (cluster 1) in all three commodities (rice, corn, and peanuts), while the Mukomuko Regency, Kepahiang, Central Bengkulu, and Bengkulu City areas had low yield potential (cluster 2) in all three commodities (rice, corn, and peanuts).

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Publication Details

Journal
JURNAL MEDIA INFOTAMA
Published
2024-10-11
DOI
https://doi.org/10.37676/jmi.v20i2.6459
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Penerapan Metode K-Medoid Dalam Pengelompokkan Data Pertanian Di Dinas Tanaman Pangan Hortikultura Dan Perkebunan Provinsi Bengkulu

Khairil Khairil, Ila Yati Beti, Hoki Dewangga
JURNAL MEDIA INFOTAMA
Data Mining and Machine Learning Applications
article

Penerapan Metode K-Medoid Dalam Pengelompokkan Data Pertanian Di Dinas Tanaman Pangan Hortikultura Dan Perkebunan Provinsi Bengkulu

Khairil Khairil, Ila Yati Beti, Hoki Dewangga
article en

Abstract

The Department of Agriculture, Food Crops, Horticulture and Plantations of Bengkulu Province has collected data on agricultural commodities in each Regency/city in Bengkulu Province which are recapitulated annually, but at the Department of Agriculture, Food Crops and Horticulture and Plantations of Bengkulu Province there is no system that can be used to classify agricultural yield data for each of these various commodities so that it is difficult to obtain information on potential harvest-producing agricultural areas in Bengkulu Province for each commodity. Therefore, a system is needed that can help provide information on the results of grouping agricultural data for each commodity so that it can be known which regency/ cities are categorized as potential crop producers in Bengkulu Province based on harvest area (Ha), production (Ton) and productivity (Ku / Ha). The implementation of K-Medoid Method in classifying agricultural data at the Department of Food Crops, Horticulture and Plantation of Bengkulu Province can provide information on the results of grouping data on agricultural areas that have potential crop yields for the last 1 year in Bengkulu Province, and can be used as a reference in providing assistance from the Department of Food Crops, Horticulture and Plantation of Bengkulu Province. From the results of clustering using the K-Medoid Method, it was found that Rejang Lebong Regency area had high yield potential (cluster 1) in all three commodities (rice, corn, and peanuts), while the Mukomuko Regency, Kepahiang, Central Bengkulu, and Bengkulu City areas had low yield potential (cluster 2) in all three commodities (rice, corn, and peanuts).

JURNAL MEDIA INFOTAMAVol. 20(2)
Universitas Dehasen Bengkulu (ID)
Zero hunger
Openalex Percentile: Top 62%
Data Mining and Machine Learning Applications
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