COAL TRADE DATA CLUSTERING USING K-MEANS (CASE STUDY PT. GLOBAL BANGKIT UTAMA)
Abstract: To compete in the
business world, especially in the distributor fields, the company must find strategy to increase the trade of
products , one of them is through the analysis of trade data. PT Global Bangkit Utama is a company engaged
in coal distributor, which has many competitors. To face the competition, PT
Global Bangkit Utama tries to find the right strategy. To make strategic
decisions, The company analyzes the information on trades data. The data used
in this study were coal trade data PT Global Bangkit Utama from January 2015 to
August 2016. One method was Data Mining to determine the
patterns of extracting information using the Clustering method. The method clusters the objects which have
similar characteristics to find the desired patterns. The process of determining the patterns of
clustering used K-Means Algorithm. K-Means algorithm is a clustering algorithm
of the data with the partition system.
K-Means algorithm was chosen because it has a high level of accuracy and
effectivity and require a relatively fast execution time due to its linerity. This research produces 8 clusters using Elbow
method. There is a characteristic equation in each cluster in the optimal
cluster that will be used as business strategy determination. The business
strategy obtained is to optimize distributors in the city of Karanganyar and
make a storage place for coal..
Penulis: Aulia Tegar Rahman
Kode Jurnal: jptinformatikadd170127