Economic needs are community needs that are used to meet daily needs. Therefore, economic needs are very important for the life of every society. There is a gap in the economic needs of the community, the government created a social assistance program which is assistance provided to the community in the form of cash or non-cash. The help is made for welfare society from inequality, especially economic inequality. So researchers will carry out a data classification of people who are eligible for social assistance. The classification will be carried out using the Naïve Bayes method. The Naïve Bayes method is a simple classification method for calculating the probability of a combination of certain data. The data to be used by researchers is community data as much as 62 community data. research done by using the Naïve Bayes method aims to classify community data that is feasible to forget social assistance. The first stage of this classification is the process of collecting community data and determining community data that will be used as a filtered sample cleaned, furthermore preprocessing data and then designing the Naïve Bayes Algorithm model. The results of data classification using the Naïve Bayes method show that the number of people who are eligible for social assistance is 14 community data and people who are not eligible for social assistance are 48 community data. These results can be a reference for determining the eligibility of the community to receive social assistance.

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