Pengelompokkan Dataset Bus Menggunakan Algoritma K-Means - Anjar Permadi; Yudhistira Arie Wijaya (Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani, 2023)
Data mining is the process of finding information by identifying patterns from datasets. The process of finding this information can be done by grouping data into several groups from a dataset which in data mining is called the clustering method. Clustering is the process of partitioning a dataset into several subsets or groups based on the similarity of the characteristics of each data in the existing groups. The clustering method used in this research is K-Means which belongs to the Partition Clustering algorithm group. This method has also been widely used in solving problems related to sales clustering, forest fires, agriculture, transportation, and so on. In this study, the k-means algorithm was used to classify the Bus BB dataset based on data collected during 2022. In the process of converting raw datasets into useful information, the Knowledge Discovery in Database (KDD) process was used. In the early stages, data cleaning will be carried out, then data selection, data transformation, and data mining will be carried out using the Rapidminer software. Modeling results were evaluated using the Davies Bouldin Index (DBI) instrument. Based on the research that has been done, it can be seen that the K-Means algorithm can be used to group BB bus datasets. Which later can be used by companies as an illustration, this research can also be used as input for companies/service providers. Abstrak Bahasa Inggris maksimum 250 kata dalam satu alinea menggunakan huruf Arial 10, spasi 1. Abstrak berisi pendahuluan singkat, tujuan, metode dan hasil secara ringkas dan jelas. Penulisan singkatan yang tidak umum tidak diperkenankan kecuali didefinisikan sebelumnya.