Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)

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The Analysis of Airport Passengers Flow by Using Spatial Temporal Graph Neural Networks and Resolving Efficient Dominating Set

Authors
A. Muklisin1, I. M. Tirta3, Dafik1, 2, *, R. I. Baihaki2, A. I. Kristiana1, 2
1Departement of Mathematics Education Postgraduate, University of Jember, Jember, Indonesia
2PUI-PT Combinatorics and Graph, CGANT, University of Jember, Jember, Indonesia
3Departement of Mathematics, University of Jember, Jember, Indonesia
*Corresponding author. Email: d.dafik@unej.ac.id
Corresponding Author
Dafik
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-174-6_2How to use a DOI?
Keywords
Spatial Temporal Neural Networks; Resolving Efficient Dominating Set; Airport Passengers Flow
Abstract

The advanced development of airport infrastructure is intended to improve airport services for air flight costumers. Thus, it is compulsory to maintain the sustainability system for service establishment. The purpose of this research is to apply the concept of Spatial Temporal Graph Neural Network (STGNN) integrated with Resolving Efficient Dominating Set (REDS) to analyze density of airport passengers flow. This research method includes the analytical and the experimental techniques. The input data involves the number of in-coming and out-coming airline, number of schedule, number of passengers and the weather. The results shows that the use of the Spatial Temporal Graph Neural Network and Resolving Efficient Dominating Set are effective tools for Airport Passengers Flow analysis, with cascadeforwardnet ANN (665) get MSE TEST 1.0328 × - 09 .

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
Series
Advances in Intelligent Systems Research
Publication Date
22 May 2023
ISBN
10.2991/978-94-6463-174-6_2
ISSN
1951-6851
DOI
10.2991/978-94-6463-174-6_2How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - A. Muklisin
AU  - I. M. Tirta
AU  - Dafik
AU  - R. I. Baihaki
AU  - A. I. Kristiana
PY  - 2023
DA  - 2023/05/22
TI  - The Analysis of Airport Passengers Flow by Using Spatial Temporal Graph Neural Networks and Resolving Efficient Dominating Set
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 3
EP  - 20
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_2
DO  - 10.2991/978-94-6463-174-6_2
ID  - Muklisin2023
ER  -