Clustering Ornamental Plants Turnover Data using K-Means Algorithm

Abstract

Ornamental plants are a commodity with high production in Indonesia, with a 17.61 million stalk increase recorded in 2018. (9.55%). Ornamental plants have capability enterprise possibilities in Indonesia as properly. The increase and decrease in ornamental plant turnover can be attributed to a variety of factors such as beauty awareness, the development of the tourism industry, ornamental plant trends, and the construction of housing and hotel complexes. A few of the factors mentioned can have an indirect impact on the sustainability of the ornamental plant business. To resolve these concerns, the grouping method was used with K-Means Clustering to determine the equation of ornamental plant turnover data based on plant commodities and monthly turnover values. Clustering with K-Means Algorithm is used in this study to group turnover data based on crop commodities and turnover value. The WEKA application's grouping results utilizing the K-Means Clustering Algorithm resulted in two clusters with values of 11% (8 data) and 89% (66 data) from a total of 74 data, where the two cluster values appeared after three time iterations.