Open AccessImplementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency
Author(s) -
Ahmad Ari Aldino,
Dedi Darwis,
Agung Tri Prastowo,
C. Sujana
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1751/1/012038
Subject(s) - cluster analysis , cluster (spacecraft) , agriculture , distribution (mathematics) , crop , hierarchical clustering , geography , mathematics , agricultural engineering , statistics , computer science , forestry , engineering , mathematical analysis , archaeology , programming language
South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km 2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district.