Prediction of Domestic Passengers at Kualanamu International Airport Using Long Short Term Memory Network
| Main Authors: | Sunardi, Sunardi; Akademi Teknik dan Keselamatan Penerbangan Medan, Dwiyanto, Dwiyanto; Akademi Teknik dan Keselamatan Penerbangan Medan, Sinambela, Marzuki; Department of Physic, FMIPA, Universitas Sumatera Utara, Jamaluddin, Jamaluddin; Universitas Methodist, Manalu, Darwis Robinson; Universitas Methodist Indonesia |
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| Other Authors: | LPPM Unika Santo Thomas Medan |
| Format: | Article info application/pdf Journal |
| Bahasa: | ind |
| Terbitan: |
LPPM UNIKA Santo Thomas Medan
, 2019
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| Subjects: | |
| Online Access: |
http://ejournal.ust.ac.id/index.php/Jurnal_Means/article/view/566 http://ejournal.ust.ac.id/index.php/Jurnal_Means/article/view/566/pdf12x http://ejournal.ust.ac.id/index.php/Jurnal_Means/article/view/566/pdf21x |
Daftar Isi:
- Domestic passenger forecasting provides key input into decisions of daily operation management and infrastructure planning of airports and air navigation services and for aircraft ordering and design. Planning for the future is one of the most important keys to success, forecasting is the way. The goal of this study to predict the number of domestic passengers at Kualanamu International Airport. The time-series data were employed from Badan Pusat Statistik (BPS). The result is then discussed in the context of the potential use of the proposed for a new perspective for the predicting of domestic passengers at Kualanamu International Airport, Indonesia. The machine learning approach using long short term memory (LSTM) presents a useful way of observing the domestic passenger predict the passenger time series.