Clustering Spatial Temporal Distribution of Fishing Vessel Based lOn VMS Data Using K-Means

Abstract

Management of sustainable marine resources is a national and global problem, and fisheries management has a complex issue, more research is need with a more comprehensive approach. Through the Ministry of Marine Affairs and Fisheries, the Government of Indonesia has made the Vessel Monitoring System (VMS). VMS data contains the position, movement, and activity of the fishing vessels utilized in this research. Data mining techniques and machine learning are using, and this study consists of three steps: i) Finding the number of optimum clusters by the Elbow Method, ii) Conducting clustering with the K-Means algorithm with the optimum k-value that has set, iii) Analyze the distribution of VMS data spatially and temporally. Overall, the optimum number of clusters obtained is 7 with the results of the compactness of the cluster members the percentage is 90.7%, spatially the distribution of VMS data in the Fisheries Management Area WPPNRI-711 is uneven and temporally very volatile. The results of this study can provide information about the intensity and location of fishing activity and prevent overfishing.