ANALISIS INFLASI MENGGUNAKAN DATA GOOGLE TRENDS DENGAN MODEL ARIMAX DI DKI JAKARTA
DOI:
https://doi.org/10.29244/ijsa.v4i3.694Keywords:
ARIMAX, forecasting, Google Trends, InflationAbstract
Inflation is an important economic indicator in showing the economic symptoms of a region's price level. DKI Jakarta is the capital of Indonesia chosen as the center of the economic barometer because it can provide the greatest contribution and influence on the Indonesian economy. The ARIMAX model was used for forecasting by adding independent variables in the Google trends data. Google trends data were explored based on seven expenditure groups published by IHK. The purpose of this study was to determine the effect of forecast Google trends using BPS inflation data in DKI Jakarta. The result of the exploration of Google Trends data was forecasted to get the best forecast model results. The result of data analysis indicates that the forecast results approached the original BPS data with the best forecast model is ARIMAX (2.0.3) all variables X. Google Trends data can be used as forecasting but cannot be used as a reference policy decision.
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References
[BPS] Badan Pusat Statistik. (2017). Indeks Harga Konsumen dan Inflasi DKI Jakarta. Jakarta(ID): BPS.
[BPS] Badan Pusat Statistik. (2019). Perkembangan Indeks Harga Konsumen Provinsi DKI Jakarta. Jakarta(ID): BPS.
Choi, H., & Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, 88: 2–9.
Eksiandayani, S. (2016). Pemodelan Peramalan Inflasi Umum dan Inflasi menurut Kelompok Pengeluaran Indonesia dengan Metode Hibrida ARIMAX [tesis]. Surabaya(ID): Institut Sepuluh Nopember.
Pati, R. K., & Padhi, S. S. (2017). Quantifying potensial tourist behavior in choice of destinition using Google Trends. J. Tourism Management Perspecives, 24: 34–47.
Pratidina. (2012). Analisis pengaruh guncangan eksternal dan internal terhadap inflasi di Indonesia [tesis]. Bogor(ID): Institut Pertanian Bogor.
Stephani, C., Suharsono, A., & Suhartono. (2015). Peramalan Inflasi Nasional Berdasarkan Faktor Ekonomi Makro Menggunakan Pendekatan Time Series Klasik dan ANFIS. J. Sains Dan Seni ITS, 4(1): 67–72.
Vicente, M. R., Lopez-Menendez, A. J., & Perez, R. (2015). Forecasting unemployment with Internet Search Data: Does it help to Improve Predictions when Job Destruction is Skyrocketing? J. Technological Forecasting and Social Change, 93: 132–139.
Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Method 2nd Edition. New York(US): Pearson Education, Inc.