Vessel Classifying and Trajectory Based on Automatic Identification System Data

Natalia Damastuti; Aulia Siti Aisjah; Agoes Masroeri

doi:10.1088/1755-1315/830/1/012049

Vessel Classifying and Trajectory Based on Automatic Identification System Data

Natalia Damastuti^*, Aulia Siti Aisjah, Agoes Masroeri

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship's trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.

Original language	English
Article number	012049
Journal	IOP Conference Series: Earth and Environmental Science
Volume	830
Issue number	1
DOIs	https://doi.org/10.1088/1755-1315/830/1/012049
Publication status	Published - 4 Oct 2021
Event	5th International Conference on Science, Infrastructure Technology and Regional Development 2020, ICoSITeR 2020 - South Lampung, Virtual, Indonesia Duration: 23 Oct 2020 → 25 Oct 2020

Keywords

AIS
Automatic Identification System
Classification
Data mining
Vessel
XG-Boost

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10.1088/1755-1315/830/1/012049

Cite this

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title = "Vessel Classifying and Trajectory Based on Automatic Identification System Data",

abstract = "Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship's trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.",

keywords = "AIS, Automatic Identification System, Classification, Data mining, Vessel, XG-Boost",

author = "Natalia Damastuti and Aisjah, {Aulia Siti} and Agoes Masroeri",

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 5th International Conference on Science, Infrastructure Technology and Regional Development 2020, ICoSITeR 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2021",

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doi = "10.1088/1755-1315/830/1/012049",

language = "English",

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journal = "IOP Conference Series: Earth and Environmental Science",

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TY - JOUR

T1 - Vessel Classifying and Trajectory Based on Automatic Identification System Data

AU - Damastuti, Natalia

AU - Aisjah, Aulia Siti

AU - Masroeri, Agoes

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2021/10/4

Y1 - 2021/10/4

N2 - Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship's trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.

AB - Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship's trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.

KW - AIS

KW - Automatic Identification System

KW - Classification

KW - Data mining

KW - Vessel

KW - XG-Boost

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U2 - 10.1088/1755-1315/830/1/012049

DO - 10.1088/1755-1315/830/1/012049

M3 - Conference article

AN - SCOPUS:85117392609

SN - 1755-1307

VL - 830

JO - IOP Conference Series: Earth and Environmental Science

JF - IOP Conference Series: Earth and Environmental Science

IS - 1

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T2 - 5th International Conference on Science, Infrastructure Technology and Regional Development 2020, ICoSITeR 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Vessel Classifying and Trajectory Based on Automatic Identification System Data

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Keywords

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