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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Pratiwi, Indriani Wahyu Nur
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODEL SELECTION FOR B-SPLINE REGRESSION USING AKAIKE INFORMATION CRITERION (AIC) METHOD FOR IDR-USD EXCHANGE RATE PREDICTION Pratiwi, Indriani Wahyu Nur; Zuliana, Sri Utami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp25-34

Abstract

Exchange rate data is a collection of information about the exchange rate the foreign currency which collected by time. Autoregressive Integrated Moving Average (ARIMA) is a well-known time series analysis. Several assumptions that need to be checked before running the ARIMA model are stationarity, normality, and white noise. B-spline regression is a method of modeling time series data without considering assumptions. This research aims to create a forecasting model for Rupiah exchange rate against US Dollar using B-spline regression. The B-spline regression model was generated with a combination of degrees two to four and a maximum of four knots. After that, the optimal model is selected using the Akaike Information Criterion (AIC) score. The performance of the selected model is validated using Mean Absolute Percentage Error (MAPE) values. The optimal degree is 3 (quadratic) and the optimal number of knot points is two-knot points with an AIC value of 857.8322 and a MAPE value of 0.0148376. The best model is:
Co-Authors Zuliana, Sri Utami