Application of Data Mining Using the K-Means Clustering Algorithm for Opening Industrial Classes in Vocational High Schools - Dian Sediana; Aan Rosydiana; Christina Juliane (Indonesian Journal of Artificial Intelligence and Data Mining, 2022)
Vocational High School has a goal to enter the world of work, meaning that it must have a skill program to be relevant to the industrial world. However, adapting to the industrial world is difficult, one of the things that is happening between industries is increasing. Various efforts continue to be made, among others, by establishing an industrial class, the formation of an industrial class is expected to produce students who have competencies in accordance with the standards required by the collaborating industries. The formation of an industrial class can be done by applying data mining methods, in order to form the right industrial class and in accordance with predetermined criteria. This study aims to classify new student registration data at State Vocational Schools at the Regional Education Office XIII Branch of West Java Province in 2022 and the results of the grouping are used to form industrial classes. The clustering process is carried out using the K-Means algorithm and cluster analysis is carried out with the help of RapidMiner software. The results showed that the data clustering was formed into 4 clusters. The cluster that has the highest number is cluster 1 and the cluster that has the lowest number is cluster 0. There are variables used for data grouping, including school variables and expertise programs, from these variables it is obtained that the schools selected by students are based on the highest order and have expertise programs contained in their clusters, which need to be considered when opening industrial classes.