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Submitted
Jan 12, 2021
Accepted
Jan 31, 2021
Published
Jan 31, 2021
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Abstract

Domestic passengers are objects whose travel / flight transportation services only cover the domestic area. The increase or decrease in the number of domestic passengers is usually influenced by the occurrence of intervention. This research uses the intervention analysis. Intervention analysis is the time series analysis to model data that is determined by the presence of an intervention. Intervention analysis is one of the time series analysis to model data that are affected by the occurrence of a particular event in a short period of time, such as accidents, natural disasters, and promotions. This research is used to establish intervention model with pulse function of passengers of domestic Sultan Hasanuddin Airport. The result of the research were obtained the model Seasonal ARIMA .There were 6 intervention times during 2006 - 2018, by entering the intervention order b = 0, s = 0, and r = 1 based on the smallest AIC value is -303,66 with MAPE value is 6,1023.

Keywords

Domestic passanger Seasonal ARIMA MAPE Intervention analysis pulse function

Article Details

How to Cite
Andi Ferosita Sustrisno, Rais, & Setiawan, I. (2021). Intervention Model Analysis The Number of Domestic Passengers at Sultan Hasanuddin Airports. Parameter: Journal of Statistics, 1(1), 41-49. https://doi.org/10.22487/27765660.2021.v1.i1.15436

References

  1. Adisasmita, R. (2010). Analisis Kebutuhan Transportasi. Makassar: Universitas Hasanuddin.
  2. Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis Forecasting and Control. San Fransisco: Holden-Day.
  3. Box, J. R. (2008). Time Series Analysis: Forecasting and Control (4th ed.). Canada: John Wiley & Sons, Inc.
  4. Badan Pusat Statistik. (2019). Transportasi pada jumlah penumpang pesawat domestik, Badan Pusat Statistik, Jakarta.
  5. Ekayanti. (2014). Analisis Model Intervensi Fungsi Step Untuk Peramalan Kenaikan Tarif Dasar Listrik (Tdl) Terhadap Besarnya Pemakaian Listrik. Pontianak:Universitas Tanjugpura.
  6. Novidian. (2015). Analisis Intervensi Kenaikan Harga Bbm Bersubsidi Pada Data Inflasi Kota Semarang. Semarang:Universitas Diponegoro.
  7. Pankratz, A. (1991). Forecasting with Dynamic Regression Models. John Wiley and Sons, Inc., Canada.
  8. Suhartono & Nuvitasari. (2007). Evaluasi Dampak Krisis Moneter, Bom Bali I dan II terhadap Jumlah Kunjungan Wisatawan ke Bali dengan Model Intervensi Multi Input. Jurnal Ilmiah MatStat.