Peramalan Inflasi Kota Malang Menggunakan Autoregressive Integrated Moving Average Exogenous dengan Efek Variasi Kalender

Authors

  • Marieta Monica
  • Agus Suharsono Departemen Statistika Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
  • Andi Tenri Ampa

DOI:

https://doi.org/10.24256/jpmipa.v10i2.2988

Keywords:

ARIMAX, Inflasi, Kestabilan Nilai Rupiah, Variasi Kalender.

Abstract

Abstract:

Bank Indonesia aims to achieve and maintain rupiah stability where the prices of goods and services are reflected in the development of inflation. The development of inflation must always be considered in determining monetary policy. This study used inflation data from Malang City from January 2012 to May 2019. This time series data was analyzed using the Autoregressive Intergrated Moving Average with Exogenous (ARIMAX) method. The modeling results show that inflation that occurs this month, is influenced by the previous 1 to 4 months. This is due to seasonal effects, and variations in the calendar before Idul Fitri.

Abstrak:

Bank Indonesia memiliki tujuan untuk mencapai dan memelihara kestabilan nilai rupiah. Kestabilan nilai rupiah adalah kestabilan terhadap harga-harga barang dan jasa yang tercermin dari perkembangan laju inflasi. Perkembangan inflasi harus selalu diperhatikan dalam penentuan kebijakan moneter. Penelitian ini menggunakan data inflasi Kota Malang bulan Januari 2012 hingga Mei 2019. Data time series ini dianalisis dengan metode Autoregressive Intergrated Moving Average with Exogenous (ARIMAX). Hasil pemodelan menunjukkan inflasi yang terjadi pada bukan yang berjalan, dipengaruhi 1 hingga 4 bulan sebelumnya. Hal ini disebabkan efek musiman, dan variasi kalender sebelum hari raya Idul Fitri.

References

Ali, Mustafa. “Arima Vs. Arimax – Which Approach Is Better to Analyze and Forecast Macroeconomic Time Series.†In Proceedings of 30th International Conference Mathematical Methods in Economics, 136–40. Czech Republic: Silesian University in Opava, School of Business Administration in Karviná, 2012. https://www.academia.edu/10595154/ARIMA_vs_ARIMAX_which_approach_is_better_to_analyze_and_forecast_macroeconomic_time_series.

Aras, Serkan, and Paulo J. G. Lisboa. “Explainable Inflation Forecasts by Machine Learning Models.†Expert Systems with Applications 207 (November 30, 2022): 117982. https://doi.org/10.1016/j.eswa.2022.117982.

Bank Indonesia. “Inflasi.†Definisi Inflasi. Accessed December 6, 2022. https://www.bi.go.id/id/fungsi-utama/moneter/inflasi/Default.aspx.

———. “Moneter.†Tujuan. Accessed December 6, 2022. https://www.bi.go.id/id/fungsi-utama/moneter/default.aspx.

Bank Indonesia and Kementerian Pendidikan dan Kebudayaan. Guidebook for Senior High School / MA Economics Teacher Contents of Central Banking. Jakarta, 2014.

Chen, Min, Shiwen Mao, and Yunhao Liu. “Big Data: A Survey.†Mobile Networks and Applications 19, no. 2 (April 1, 2014): 171–209. https://doi.org/10.1007/s11036-013-0489-0.

Ergemen, Yunus Emre. “Forecasting Inflation Rates with Multi-Level International Dependence.†Economics Letters 214 (May 1, 2022): 110456. https://doi.org/10.1016/j.econlet.2022.110456.

Junttila, Juha. “Structural Breaks, ARIMA Model and Finnish Inflation Forecasts.†International Journal of Forecasting 17, no. 2 (April 1, 2001): 203–30. https://doi.org/10.1016/S0169-2070(00)00080-7.

Kepala Biro Humas dan Hukum Badan Pusat Statistik. “Konsep Inflasi.†Badan Pusat Statistik. Accessed December 6, 2022. https://www.bps.go.id/subject/3/inflasi.html#subjekViewTab1.

Lee, Muhammad Hisyam, and Nor Hamzah. “Calendar Variation Model Based on Arimax for Forecasting Sales Data with Ramadhan Effect.†In Proceedings of the Regional Conference on Statistical Sciences 2010 (RCSS’10), 349–61. Malaysia: Malaysia Institute of Statistics, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 2022. https://www.researchgate.net/profile/Nor-Hamzah-4/publication/267786379_Calendar_variation_model_based_on_ARIMAX_for_forecasting_sales_data_with_Ramadhan_effect/links/54bce3100cf24e50e940b402/Calendar-variation-model-based-on-ARIMAX-for-forecasting-sales-data-with-Ramadhan-effect.pdf.

Silfiani, Mega, and Suhartono Suhartono. “Peramalan Inflasi di Indonesia Aplikasi Metode Ensembel untuk.†Jurnal Sains dan Seni ITS 1, no. 1 (September 11, 2012): D171–76. https://doi.org/10.12962/j23373520.v1i1.1965.

Spiegel, Murray R., and Larry J. Stephens. Schaum’s Outlines Teori Dan Soal-Soal Statistik. Jakarta: Erlangga, 2007.

Stephani, Clara Agustin, Agus Suharsono, and Suhartono Suhartono. “Peramalan Inflasi Nasional Berdasarkan Faktor Ekonomi Makro Menggunakan Pendekatan Time Series Klasik dan ANFIS.†Jurnal Sains dan Seni ITS 4, no. 1 (March 13, 2015): D67–72. https://doi.org/10.12962/j23373520.v4i1.8873.

Trull, Óscar, J. Carlos García-Díaz, and Alicia Troncoso. “Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain.†Applied Sciences 10, no. 7 (January 2020): 2630. https://doi.org/10.3390/app10072630.

Wang, Z., I. Koprinska, and M. Rana. “Pattern Sequence-Based Energy Demand Forecast Using Photovoltaic Energy Records.†In Proceedings of the International Conference on Artificial Neural Networks, Nagasaki, Japan, 11–14, 2017.

Wei, William W. S. Time Series Analysis: Univariate and Multivariate Methods. Pearson Addison Wesley, 2006.

Winormia, Tresnaeni. “Peramalan Tingkat Inflasi Provinsi Jawa Barat Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation.†Other, Universitas Pendidikan Indonesia, 2018. https://doi.org/10/S_MAT_1400598_Appendix.pdf.

Downloads

Published

27-10-2022

How to Cite

Monica, M., Suharsono, A., & Ampa, A. T. (2022). Peramalan Inflasi Kota Malang Menggunakan Autoregressive Integrated Moving Average Exogenous dengan Efek Variasi Kalender. Al-Khwarizmi : Jurnal Pendidikan Matematika Dan Ilmu Pengetahuan Alam, 10(2), 149–162. https://doi.org/10.24256/jpmipa.v10i2.2988

Citation Check