Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu

Abstract

Transportation is an activity of moving things such as humans, animals, plants and goods from one place to another. To be able to implement transportation, we need a means of transportation that suits our needs. For in Indonesia, people are more inclined to land transportation. That's because land transportation already has a lot of vehicles. Land transportation already has many vehicles that can be used, both for private and for the public. Each vehicle has its uses and risks as well. Therefore we will do a data cluster from the trains. We chose the train, because the risk from using the train is very small, meaning that there is a lot of public interest in trains. So we want to do a cluster on rail passengers. The cluster that we do is to group passenger data based on the similarity of passenger data. We will do the cluster using the K-Means method. The K-Means method is very suitable when used to perform a cluster. K-Means will process widgets that are made according to the needs of the research. So after we enter the method in the widget pattern, the widget will process it to output the results from the cluster that we created. The cluster process using the K-Means method will be applied using the orange application. After we apply it, the data will later be clustered, we will cluster data as many as 3 clusters. Then the incoming data will appear in clusters 1, 2 and 3, both from business and executive classes

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