Volume 30 Number 2, 2025

Unravelling Indonesia's CPI Inflation Dynamics through ARIMA‑Based Intervention Analysis

Rezzy Eko Caraka ^a,b,c,*, Prana Ugiana Gio ^d, Andrari Grahitandarui ^a, Budi Nugroho ^a, Nimas Ayu Untariyati ^a, Rumanintya Lisaria Putri ^e, Amos Lukas ^f, Dian Yudha Risdianto ^g, Hallen Naafi Aliya Rachman ^h, Ivana Sakura India Margareth  ^h, Irish Shanty Kinsella Puteri ^h, Yosefina Pradjanata ^h, Windy Maya Esteryna Pasaribu ^h, Gumgum Darmawan ^h, Rung Ching Chen ^c, Bens Pardamean ^i,j

 
^{a Research Center for Data and Information Sciences, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN), Bandung, West Java 40135, Indonesia
rezzy.eko.caraka@brin.go.id; andr001@brin.go.id ; budi045@brin.go.id; nima004@brin.go.id
b School of Economics and Business, Telkom University, Bandung 40257, Indonesia 
c Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
crching@cyut.edu.tw
d Department of Mathematics, Universitas Sumatera Utara, Medan, 20155, Indonesia
prana@usu.ac.id
e Research Center for Cooperative, Corporation and People's Economy, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
ruma004@brin.go.id
f Research Center for Agroindustry, Research Organization for Agriculture and Food, National Research and Innovation Agency (BRIN), Banten,15314, Indonesia
amos001@brin.go.id
g Research Center for Space, Research Organization for Aeronautics and Space, National Research and Innovation Agency (BRIN), Bandung 40135, Indonesia
dian022@brin.go.id
h Department of Statistics, Faculty of Mathematics and Natural Science, Padjadjaran University, Sumedang, West Java 45361, Indonesia
hallen21001@mail.unpad.ac.id; yosefina.pradjanata@gmail.com ; ivanasakura88@gmail.com; irishshanty334@gmail.com; windy21004@mail.unpad.ac.id; gumstat@gmail.com
i Department of Computer Science, BINUS Graduate Program-Master of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia
j Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
bpardamean@binus.edu}
 

ABSTRACT

Background: The COVID-19 pandemic has significantly influenced the Consumer Price Index (CPI) of Indonesia, resulting in substantial fluctuations in inflation. These variations, driven by both external and unpredictable factors, have profound effects on the broader economy and society. Methods: This study employs the Autoregressive Integrated Moving Average (ARIMA) model to forecast the general CPI inflation in Indonesia. To assess the impact of major events, such as the COVID-19 pandemic, intervention analysis is incorporated into the model. Results: The ARIMA model selected for forecasting the CPI inflation in Indonesia demonstrates strong predictive accuracy, with an AIC value of 250.44 and a MAPE of 11.05%. These results indicate that the model offers a high level of reliability for forecasting inflation. The model was further used to predict CPI inflation for the first four months of 2023 (January to April), providing valuable short-term forecast understanding. Concluding Remarks: The ARIMA model, enhanced with intervention analysis, provides a robust tool for forecasting CPI in Indonesia, highlighting its effectiveness for short-term inflation predictions. With a low AIC and an acceptable MAPE, the model offers actionable insights for policymakers and stakeholders, supporting informed decisions aimed at mitigating the adverse effects of inflation volatility in the post-pandemic economy.

JEL Classifications: C22; E31; E37; I18

Keywords: intervention, time series, COVID-19, CPI, data analysis

Cite this article: 

Caraka, R. E., Gio, P. U., Grahitandaru, A., Nugroho, B., Untariyati, N. A., Putri, R. L., Lukas, A., Risdianto, D. Y., Rachman, H. N. A., Margareth, I. S. I., Puteri, I. S. K., Pradjanata, Y., Pasaribu, W. M. E., Darmawan, G., Chen, R. C., & Pardamean, B., 2025, Unravelling Indonesia's CPI Inflation Dynamics through ARIMA‑Based Intervention Analysis, International Journal of Business, 30(2), 003. https://doi.org/10.55802/IJB.030(2).003