skip to main content

PENERAPAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) UNTUK MERAMALKAN PENERBANGAN DOMESTIK PADA TIGA BANDAR UDARA DI PULAU JAWA

*Adinda Putri Muzdhalifah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tarno Tarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Puspita Kartikasari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2022 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
The number of flights is a thing to measure the marketing performance of aviation services. Forecasting the number of flights is done so that airlines can make decisions in increasing the number of passengers and revenue. Forecasting the number of flights at various airports has relationship between time and location. The suitable method for forecasting the number of flights is Generalized Space Time Autoregressive (GSTAR) method. GSTAR is a method that used for forecasting time series data that has a relationship between time and location and has heterogeneous characteristics. This study applied the GSTAR method to model and forecast the number of domestic flights at three airports in Java, namely Husein Sastranegara Airport Bandung, Ahmad Yani Semarang, and Juanda Surabaya. The research chose those three airports because the impact of Covid-19 is very severe in that area. The weight used in this study is the distance inverse weight. The resulting model is a model with differencing 1, autoregressive order 1, and spatial order limited to 1 so that the model formed is the GSTAR model (11)-I(1). The GSTAR (11)-I(1) meets the assumptions of residual white noise and normal multivariate. The model also has sMAPE values for each airport: 2.60%, 4.18%, and 9.89%. Therefore, it can be concluded that the forecasting results of Husein Sastranegara Airport Bandung, Ahmad Yani Airport Semarang, and Surabaya Juanda Airport are very accurate.
Fulltext View|Download
Keywords: Number of flights; airports; distance inverses; forecasting; GSTAR; sMAPE

Article Metrics:

  1. Amin, P.A. (2013). Analisis Pengaruh Tarif Penerbangan, Jumlah Penerbangan, dan Pendapatan Per Kapita dalam Meningkatkan Jumlah Penumpang. Jurnal Bisnis Strategi, 22(1)
  2. Borovkova et al. (2008). Consistency and Asymptotic Normality of Least Square Estimators in Generalized STAR Models. Statistica Neerlandica, 62(4), 482-508
  3. Chen, C., Twycross, J., & Garibaldi, J.M. (2017). A New Accuracy Measure Based on Bounded Relative Error for Time Series Forecasting. PLoS ONE, 2(3)
  4. Cryer, J.D., & Chan, KS. (2008). Time Series Analysis: With Apllication in R: Second Edition. USA: Spinger Science dan Business Media, LLC
  5. Makridakis, S., S.C. Wheelwright., & V.E. McGee. (1992). Metode dan Aplikasi Peramalan. Jakarta: Erlangga
  6. Pfeifer, P.E. & Deutsch, S.J. (1980). A Three-Stage Iterative Procedure for Space-Time Modeling. Technometrics, 22(1), 35-47
  7. Ruchjana et al. (2018). Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java. Applied Mathematics and Information Sciences an International Journal, 12(3), 607-615
  8. Schober, P., Boer, C., & Schwarte, L.A. (2018). Correlation Coefficients: Appropriatee Use and Interpretation. Correlation Coefficient in Medical Research, 30(30), 1763-1768
  9. Soejoeti. (1987). Analisis Runtun Waktu. Jakarta: Karunika Jakarta
  10. Wei, W. (2006). Time Series Analysis : Univariate and Multivariate Methods. Amerika: Pearson Education, Inc

Last update:

No citation recorded.

Last update:

No citation recorded.