Classification of Ship-Based Automatic Identification Systems Using K-Nearest Neighbors

One of vessel monitoring systems which employs predetermined equipment to discover the movements and activities of vessels is AIS (Automatic Identification System). AIS contains the ship data either static (ship name, ship size, sailing time) or dynamic data (ship speed, rate of turn, ship heading). The ship tracking information system can be accessed by public, but manual monitoring will be difficult to do, given that data is increasingly heterogeneous and complex as well as its volumes increase exponentially. As a result, a more efficient method of data mining and processing are needed. In this study, k-NN algorithm is applied with the aim of classifying ships sailing in Indonesian waters. The algorithm is tested on real time AIS database using k-NN and the neighborhood component analysis (NCA). The result shows that NCA,KNN has higher accuracy than using k-NN on original classifier.