Klasifikasi Penumpang Kereta Api DAOP 6 Yogyakarta Berdasarkan Kelas Stasiun Menggunakan KNN - ichsanudin, Ichsanudin; supatman, supatman (JATISI, 2024)
Transportation companies continue to adapt to technological developments to improve services to service users. The train is the most crossed mass transportation for service users today. Because of the level of timeliness, comfort and traffic-free so that the train becomes the mainstay mode of transportation for service users. The more service users, of course, the train must improve services to improve services. Therefor The author wants to conduct research on the classification of train passengers, the classification algorithm is used to analyze the number of passengers at the station. This research was conducted using the K-Nearest Neighbor method in determining the number of passengers based on the station class. The K-Nearest Neighbor method is a technique for finding the k target members in the data (training) that are closest to the test data. The dataset in this study uses data sourced from the Passenger Transport Unit DAOP 6 Yogyakarta PT Kereta Api Indonesia from the volume of up and down passengers from 2016 to 2023. The classification results with the K-Nearest neighbor method obtained very good results with an accuracy rate of 93%.