Prediction of Domestic Passengers at Kualanamu International Airport Using Long Short Term Memory Network


  • Sunardi Sunardi Akademi Teknik dan Keselamatan Penerbangan Medan
  • Dwiyanto Dwiyanto Akademi Teknik dan Keselamatan Penerbangan Medan
  • Marzuki Sinambela Department of Physic, FMIPA, Universitas Sumatera Utara
  • Jamaluddin Jamaluddin Universitas Methodist
  • Darwis Robinson Manalu Universitas Methodist Indonesia



passenger, LSTM, machine learning


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.


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How to Cite

Sunardi, S., Dwiyanto, D., Sinambela, M., Jamaluddin, J., & Manalu, D. R. (2019). Prediction of Domestic Passengers at Kualanamu International Airport Using Long Short Term Memory Network. MEANS (Media Informasi Analisa Dan Sistem), 4(2), 165–168.



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