Abstract
Indonesia is an agricultural country where the majority of its population makes a living from agriculture. The agricultural sector is a very important sector for economic development in an agricultural country like Indonesia. Poor irrigation facilities greatly affect the results of the agricultural sector. Crop quality is based on many factors such as the characteristics of the irrigation process, including the amount of air and irrigation time. Overwatering irrigation can cause air wastage, soil freezing disease, yellowing of plant leaves, wilting of plant leaves, and many other problems. K-Means clustering is a method used to group data into one or more groups or clusters. The advantages of the K-Means algorithm are that it is easy and simple to implement, scalability, speed in convergence, and the ability to adapt to sparse data. K-Means to group agricultural land based on soil fertility and rainfall data, found that this grouping can help in more efficient irrigation planning. The clustering results show that agricultural land can be divided into three main clusters based on soil fertility and irrigation. Soil fertility is formed into three clusters based on the level of soil fertility using the Kmeans algorithm which can also be effective in helping in the Indonesian agricultural sector. By adding technological elements, the results provided will of course be even better.
Published Version
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