Implementation of the K-Means Clustering Method in Salt Production Area Grouping Applications
Abstract
Background: Indonesia has a large land and sea area that has the potential for salt production. Efforts to increase salt production are still not optimal because there are still several areas that produce salt products in small quantities even though they have a large land area and a large number of farmers, so for areas like this, it is necessary to carry out guidance so that they can increase the amount of salt production. So far, no system has been used to group salt-producing regions, so it is impossible to know which areas still have the potential to increase their production. Objective: This research aims to create an application system to classify salt-producing regions.
Methods: The clustering method used is K-Means, where this method can group datasets into several predetermined clusters.
Result: The results of application testing carried out using 10 salt-producing region datasets obtained 4 regions with small production volume clusters, 3 regions with sufficient production volume clusters, 3 regions with large production volume clusters.
Conclusion: The application of grouping salt-producing areas using the K-Means method can help the process of grouping salt-producing regions easier, faster, and more objectively.
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