Analisis Kalman filter berbasis Google Trends untuk Prediksi Kedatangan Wisatawan Mancanegara di Indonesia Pasca Pandemi

 Abstract views: 196

Authors

  • Evita Purnaningrum Universitas PGRI Adi Buana
  • Hanief Khoyyir Nafah Universitas PGRI Adi Buana

DOI:

https://doi.org/10.36456/jstat.vol14.no2.a4956

Keywords:

Peramalan, Kalman filter, Pariwisata, Pandemi, Google Analitik

Abstract

Pada tahun 2019 kunjungan wisatawan mancanegara (wisman) ke Indonesia mengalami peningkatan yang cukup signifikan. Sehingga, pariwisata diprediksi menjadi salah satu penopang terbesar dari penerimaan negara. Namun, saat wabah Coronavirus terjadi di akhir tahun 2019, sektor ini menjadi sektor industri yang paling terdampak dengan penurunan yang sangat tajam dan perkirakan akan membaik sekitar tahun 2035 hingga 2045. Kejadian tersebut mendorong penelitian untuk merumuskan model proyeksi terbaik bagi wisatawan asing pasca pandemi dengan menggunakan metode Kalman filter. Kalman filter merupakan model state space yang dapat diulang untuk menghasilkan nilai akurasi estimasi yang tinggi. Model ini didukung oleh analisis google trends yang mampu menangkap minat negara lain terhadap pariwisata Indonesia, terutama di masa pandemi. Hasil penelitian menunjukkan bahwa meskipun pandemi, beberapa negara masih memiliki minat terhadap objek wisata di Indonesia. Selain itu, Kalmanfilter memiliki akurasi yang tinggi dalam peramalan wisatawan asing

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Author Biography

Evita Purnaningrum, Universitas PGRI Adi Buana

Manajemen, Fakultas Ekonomi

References

Alamsyah, A., & Friscintia, P. B. A. (2019). Artificial neural network for Indonesian tourism demand forecasting. 2019 7th International Conference on Information and Communication Technology, ICoICT 2019. https://doi.org/10.1109/ICoICT.2019.8835382

Badan Pusat Statistik. (2019). Jumlah kunjungan wisman ke Indonesia Desember 2019 mencapai 1,38 juta kunjungan. Retrieved from Perkembangan Pariwisata dan Transportasi Nasional Desember 2019 website: https://www.bps.go.id/pressrelease/2020/02/03/1711/jumlah-kunjungan-wisman-ke-indonesia-desember-2019-mencapai-1-38-juta-kunjungan-.html

Bradford, E., & Imsland, L. (2018). Economic Stochastic Model Predictive Control Using the Unscented Kalman Filter. IFAC-PapersOnLine, 51(18), 417–422. https://doi.org/10.1016/j.ifacol.2018.09.336

Cachia, R., Compañó, R., & Da Costa, O. (2007). Grasping the potential of online social networks for foresight. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2007.05.006

De Vita, G. (2014). The long-run impact of exchange rate regimes on international tourism flows. Tourism Management. https://doi.org/10.1016/j.tourman.2014.05.001

Durbin, J., & Koopman, S. J. (2012). Time Series Analysis by State Space Methods: Second Edition. Aging. https://doi.org/10.1017/CBO9781107415324.004

Fauziyah, & Purnaningrum, E. (2021). Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method. Jurnal Matematika MANTIK, 7(1), 20–30. https://doi.org/https://doi.org/10.15642/mantik.2021.7.1.20-30

Haqiq, A., & Pharmasetiawan, B. (2019). Data Analytics for Forecasting Arrival of Tourism Visit in Indonesia. Proceeding - 2019 International Conference on ICT for Smart Society: Innovation and Transformation Toward Smart Region, ICISS 2019. https://doi.org/10.1109/ICISS48059.2019.8969795

Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. In Forecasting, Structural Time Series Models and the Kalman Filter. https://doi.org/10.1017/cbo9781107049994

Helske, J. (2017). KFAS: Exponential family state space models in R. Journal of Statistical Software. https://doi.org/10.18637/jss.v078.i10

Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2019). Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden. Information Technology and Tourism. https://doi.org/10.1007/s40558-018-0129-4

Kharista, A., Permanasari, A. E., & Hidayah, I. (2015). The performance of GM (1,1) and ARIMA for forecasting of foreign tourists visit to Indonesia. 2015 International Seminar on Intelligent Technology and Its Applications, ISITIA 2015 - Proceeding. https://doi.org/10.1109/ISITIA.2015.7219949

Kusumawardhani, D. A., & Purnaningrum, E. (2021). Penyebaran Pengguna Digital Wallet Di Indonesia Berdasarkan Google Trends Analytics. INOVASI, 17(2), 377–385. https://doi.org/http://dx.doi.org/10.29264/jinv.v17i2.8069

Mariyono, J. (2017). Determinants of Demand for Foreign Tourism in Indonesia. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan. https://doi.org/10.23917/jep.v18i1.2042

Martins, L. F., Gan, Y., & Ferreira-Lopes, A. (2017). An empirical analysis of the influence of macroeconomic determinants on World tourism demand. Tourism Management. https://doi.org/10.1016/j.tourman.2017.01.008

Massicotte, P., & Eddelbuettel, D. (2019). gtrendsR: Perform and Display Google Trends Queries. R Package Version.

Montaño Moreno, J. J., Palmer Pol, A., Sesé Abad, A., & Cajal Blasco, B. (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema. https://doi.org/10.7334/psicothema2013.23

Nafah, H. K., & Purnaningrum, E. (2021). Penggunaan Big Data Melalui Analisis Google Trends Untuk Mengetahui Perspektif Pariwisata Indonesia Di Mata Dunia. Snhrp.

Newsome, D. (2020). The collapse of tourism and its impact on wildlife tourism destinations. Journal of Tourism Futures. https://doi.org/10.1108/JTF-04-2020-0053

Purnaningrum, E. (2018). Renewable Stock Price Model Sebagai Pendukung Investasi Saham: Studi Kasus Saham Jii. Kolegial.

Purnaningrum, E, Cahyaningtias, S., & Kusumawardhani, D. A. (2021). Augmentation time series model with Kalman filter to predict foreign tourist arrivals in East Java. Journal of Physics: Conference Series, 1869(1), 012116. https://doi.org/10.1088/1742-6596/1869/1/012116

Purnaningrum, Evita. (2020). Pendekatan Metode Kalman Filter untuk Peramalan Pergerakan Indeks Harga Saham Terdampak Pandemi Coronavirus. Majalah Ekonomi.

Purnaningrum, Evita, & Ariqoh, I. (2019). Google Trends Analytics dalam Bidang Pariwisata. Majalah Ekonomi.

Purnaningrum, Evita, & Ariyanti, V. (2020). Pemanfaatan Google Trends Untuk Mengetahui Intervensi Pandemi Covid-19 Terhadap Pasar Saham Di Indonesia. Jurnal.Unipasby.Ac.Id.

Rizal, A. A., & Hartati, S. (2017). Recurrent neural network with Extended Kalman Filter for prediction of the number of tourist arrival in Lombok. 2016 International Conference on Informatics and Computing, ICIC 2016. https://doi.org/10.1109/IAC.2016.7905712

Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management. https://doi.org/10.1016/j.tourman.2018.07.010

Supriatna, A., Hertini, E., Saputra, J., Subartini, B., & Robbani, A. A. (2019). The forecasting of foreign tourists arrival in indonesia based on the supply chain management: An application of artificial neural network and holt winters approaches. International Journal of Supply Chain Management.

Wakimin, N. F., Azlina, A. A., & Hazman, S. (2018). Tourism demand in Asean-5 countries: Evidence from panel data analysis. Management Science Letters. https://doi.org/10.5267/j.msl.2018.4.023

Wilcox, B. A., & Hamano, F. (2017). Kalman’s Expanding Influence in the Econometrics Discipline. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2017.08.106

World Health Organization (WHO). (2020). Novel Coronavirus – China.

Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management. https://doi.org/10.1016/j.tourman.2014.07.019

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Published

01/22/2022

How to Cite

Evita Purnaningrum, & Nafah , H. K. (2022). Analisis Kalman filter berbasis Google Trends untuk Prediksi Kedatangan Wisatawan Mancanegara di Indonesia Pasca Pandemi. J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 14(2), 110–115. https://doi.org/10.36456/jstat.vol14.no2.a4956