Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination

(1) * Paska Marto Hasugian Mail (Software Engineering, STMIK Pelita Nusantara, Medan, Indonesia)
(2) Bosker Sinaga Mail (Software Engineering, STMIK Pelita Nusantara, Medan)
(3) Jonson Manurung Mail (Software Engineering, STMIK Pelita Nusantara, Medan, Indonesia)
(4) Safa Ayoub Al Hashim Mail (College of Information Technology, University of Babylon, Iraq)
*corresponding author


Indonesia is the third-largest country in the world with rice production reaching 83,037,000 and became the highest production in southeast Asia spread in several provinces in Indonesia The problem found that such product has not been able to cover the needs of Indonesian people with a very high population so that in the research conducted information excavation to generate potential to the pile of data that has been described and analyzed by BPS with clustering topics. Clustering will help related parties, especially the ministry of agriculture, in determining land development priorities and can minimize the shortage of rice production nationally. Grouping process by involving the K-means algorithm to group rice production with a combination of the elbow method as part of determining the number of clusters that will be recommended with attributes supporting the area of harvest, productivity, and production. Method of researching with data cleaning activities, data integration, data transformation, and application of K-means with a combination of elbow and pattern evaluation. The results achieved based on the work description with a combination of K-Means and elbow provide cluster recommendations that are the best choice or the most optimal is iteration 2 which is the lowest rice production group with a total of 22 provinces, rice production with a medium category of 9 and production with the highest category with 3 regions


Cluster K-Means Elbow production rice



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