K-means algorithm for clustering system of plant seeds specialization areas in east Aceh

Abstract

The number of regions and types of plants in East Aceh Regency requires a data clustering process in order to easily find out which areas are most in-demand based on the type of plants. This study applies the k-means algorithm to classify the data. The data used in this study were obtained from the Department of Agriculture, Food Crops and Horticulture, East Aceh Regency. Based on the test results with k-means, the average number of iterations in the 2015-2019 data is 8,7,6,4,3 iterations for each commodity. The test results can show areas of interest for plant seeds with clusters of high demand, attractive, and less desirable. The system in this study was built based on the web using the PHP programming language.