THE APPLICATION OF THE K-MEANS METHOD FOR CLUSTERING VIAR VEHICLE SPARE PARTS SALES

Abstract

Until now motorcycles are still one of the most widely used means of transportation
by Indonesian people. One of the authorized VIAR dealers in Sulawesi is CV
Gotama VIAR Gorontalo. The company sells several VIAR-branded motorcycles
and some genuine spare parts. There are stock-outs in several types of VIAR vehicle
spare parts that are sold because many consumers buy them. There is a stacking
of stock of other types of VIAR vehicle spare parts in the warehouse because they
are not well sold. It is caused by the company experiencing confusion in
determining what types of spare parts are more and less in-demand. The purpose
of this study is to group several types of vehicle spare parts that are more and less
in demand. The K-Means method is one of the methods in partitional clustering
which works in grouping large data by dividing the data into one or more clusters.
Based on the results of this study, it can be concluded that the results obtained
explain that there are 373 types of goods categorized as more in-demand and 7
types of goods categorized as less in-demand. The system created can obtain a
system that can classify spare parts sales data using the K-Means method which is
reliable when applied.