Indeks Harga Komsumen (IHK) di Lampung Menggunakan Autoregressive Integrated Moving Average (ARIMA)

  • Mika Alvionita Sitinjak Institut Teknologi Sumatera
  • Nuramaliyah ‎ Program Studi Ilmu Aktuaria , Fakultas Ekonomi dan Bisnis, Universitas Teknologi Sumbawa

Abstract

The Consumer Price Index (CPI) is an indicator that influences economic growth. CPI is an index that calculates the average of price change of a group of goods and services consumed by households in a certain period of time. CPI is also used to measure inflation in a country. Inflation is described by changes in the CPI from time to time. To anticipate and minimize economic risks caused by inflation, forecasting will be carried out on CPI data. In this study, the CPI will be predicted for the next 6 months using the ARIMA (Autoregressive Integrated Moving Average) model. The result of this research shows that the ARIMA models that can be used to predict CPI are ARIMA (0,2,0), ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (1,2,1) . The selection of the best model is carried out based on the model that has the smallest AIC value. Based on this, the best model used to predict CPI is the ARIMA model (0,2,1) with an AIC value of 83.21. In addition, this model fulfills diagnostics with white noise residuals, so that forecasting results using this model will be more accurate.

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References

[1] Badan Pusat Statistik. Indeks Harga Konsumen (IHK) Tersedia: https://lampung.bps.go.id/indicator/3/51/1/indeks-harga-konsumen.html. 2022
[2] Bank Indonesia. Tersedia : https://www.bi.go.id/id/fungsi-utama/moneter/default.aspx. 2020
[3] A.P. Desvina dan E. Desmita, “Penerapan Metode Box-Jenkis dalam Meramalkan Indeks Harga Konsumen di Kota Pekanbaru”, Jurnal Sains Matematika dan Statistika, Vol. 1, No. 1, 2015.
[4] I. Efrilia, “Comparison Of Arima and Exponential Smoothing Holt-Winters Methods For Forecasting CPI In The Tegal City, Central Jaya”, Jurnal Ekonomi Pembangunan, Vol. 19, No. 02, 2021.
[5] D. Hatidja, “Penerapan Model Arima untuk Memprediksi Harga Saham PT. Telkom Tbk”, Jurnal Ilmiah Sains Vol.11, No. 1, 2011.
[6] S. Lalu, Dasar-Dasar Manajemen Produksi dan Operersi, Jakarta : Penerbit Salemba Empat, 2003.
[7] M.H. Mukron dkk, “Peramalan Indeks Harga Konsumen Indonesia Menggunakan Autoregressive Integrated Moving Average”, Jurnal Statistika dan Komputasi, Vol. 2, No. 1, 2021.
[8] D. Pratiwi dkk, “Perencanaan Produksi Menggunakan Model Arima dan Pengendalian Persediaan Menggunkan Program Dinamik untuk Meminimumkan Total Biaya (Studi Kasus : Produksi Amplang UD. Usaha Devi)”, Jurnal Eksponensial Vol. 4, No. 1, 2013.
[9] F. A. Razak, “Load Forecasting Using Time Series Models”, Jurnal Kujuruteraan. 21: 53-62, 2009.
[10] R. S. Tsay, Analysis of Financial Time Series, Second Edition, USA: Wiley Interscience, A John Wiley and Sons. Inc. Publication, 2005.
Published
2023-07-28
How to Cite
SITINJAK, Mika Alvionita; ‎, Nuramaliyah. Indeks Harga Komsumen (IHK) di Lampung Menggunakan Autoregressive Integrated Moving Average (ARIMA). Indonesian Journal of Applied Mathematics, [S.l.], v. 3, n. 1, p. 15-20, july 2023. ISSN 2774-2016. Available at: <https://journal.itera.ac.id/index.php/indojam/article/view/1274>. Date accessed: 19 may 2024. doi: https://doi.org/10.35472/indojam.v3i1.1274.