The Best Model of the Autoregressive Integrated Moving Average (ARIMA) Method for Predicting the Exchange Rate of the Indonesian Rupiah Against the US Dollar  (USD) for the Period July 2025 - June 2026

Desiana Putri; Meliyana Bohori

doi:10.20956/j.v22i2.48098

Authors

Desiana Putri Department of Mathematics, University of Lampung
Meliyana Bohori Department of Mathematics, University of Lampung

DOI:

https://doi.org/10.20956/j.v22i2.48098

Keywords:

exchange rate, Rupiah, USD, ARIMA, forecasting

Abstract

The exchange rate fluctuations are an important indicator that affects the stability of a country's economy, including Indonesia. This condition makes accurate exchange rate forecasting a strategic necessity in supporting economic decision-making and fiscal policy. One of the methods widely used for exchange rate forecasting is Autoregressive Integrated Moving Average (ARIMA), which has proven effective in capturing patterns and trends in historical data. Therefore, this study was conducted to find the best model for forecasting the exchange rate of the Rupiah against the US Dollar (USD) using the Autoregressive Integrated Moving Average (ARIMA) method. The data used is monthly data on the exchange rate of the Rupiah against the USD for the period January 2015 to June 2025. By identifying, estimating, and diagnosing the model, the best ARIMA model was obtained that met the white noise assumption and produced the lowest AIC/BIC value.

References

[1] Aksan, I., & Nurfadilah, K., 2020. Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Penggunaan Harian Data Seluler. Journal of Mathematics: Theory and Applications, 2(1), 5-10.

[2] AndaniI, W., & Satyahadewi, N.,2025. Peramalan Nilai Tukar Rupiah Terhadap Dolar AS Menerapkan Arima, VAR Dan Random Forest. Cendekia : Jurnal Ilmu Pengetahuan, 5(1), 204-216.

[3] Arifin, I., & Wagiana, H.,2009. Membuka Cakrawala Ekonomi. Pusat Perbukuan Departemen Pendidikan Nasional.

[4] Cahyani, Y.,2023. Penerapan Metode ARIMA (Autoregressive Integrated Moving Average) Berbasis Minitab untuk Memprediksi Tingkat Pengajuan Klaim Asuransi Kecelakaan di PT Jasa Raharja Perwakilan Malang. Seminar Nasional Pendidikan Ipa Dan Matematika (pp. 301-311). Malang: Universitas Negeri Malang.

[5] Elhakim, R. , 2020. Rediksi Nilai Tukar Rupiah Ke Dollar Amerika Serikat Menggunakan Metode Arima. MATHunesa Jurnal Ilmiah Matematika, 8(2), 145-150.

[6] Frestantiya, M. T., 2016. Peramalan Nilai Tukar Rupiah (IDR) Terhadap Dollar Amerika (USD) Menggunakan Metode Support Vector Regression (SVR) dengan Parallel Time Variant Particle Swarm Optimization (PTV-PSO) (Skripsi). Malang: Program Studi Teknik Informatika Program Studi Teknologi Informasi Dan Ilmu Komputer Universitas Brawijaya.

[7] Hayati, T., Umbara, R. F., & Sumaryatie, E.,2014. Peramalan Nilai Tukar Rupiah (IDR) Terhadap Dolar (USD) Dengan Menggunakan Metode Gabungan ARIMA (Autoregressive Integrated Moving Average) dan ANN (Artificial Neural Network) (Skripsi). Telkom University.

[8] Juanda, B., & Junaidi., 2021. Ekonometrika Deret Waktu : Teori Dan Aplikasi. Bogor: Percetakan IPB.

[9] Khoiri, H. A., 2023. Analisis Deret Waktu Univariat. Jawa Timur: Unipma Press.

[10] Normayanti., 2023. Autoregressive Intergrated Moving Average Exsogenous (Arimax) Dalam Meramalkan Pengaruh Nilai Tukar Rupiah Terhadap Indeks Harga Saham Gabungan (Ihsg) (Skripsi). Makasar: Program Studi Statistika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Negeri Makassar.

[11] Syaifudin, M., 2023. Peramalan Nilai Tukar Rupiah Terhadap Yuan China Dalam Perdagangan Bilateral Indonesia Dan China.(Skripsi) Magelang: Fakultas Ekonomi Universitas Tidar.