TY - GEN
T1 - Clustering Spatial Temporal Distribution of Fishing Vessel Based lOn VMS Data Using K-Means
AU - Sunarmo,
AU - Affandi, Achmad
AU - Sumpeno, Surya
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - 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.
AB - 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.
KW - Data Mining
KW - K-Means Algorithm
KW - Machine Learning
KW - Spasial and Temporal Pattern
KW - Unsupervised Learning
KW - Vessel Monitoring System
UR - http://www.scopus.com/inward/record.url?scp=85100900350&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT50329.2020.9331994
DO - 10.1109/ICOIACT50329.2020.9331994
M3 - Conference contribution
AN - SCOPUS:85100900350
T3 - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
SP - 1
EP - 6
BT - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Communications Technology, ICOIACT 2020
Y2 - 24 November 2020 through 25 November 2020
ER -