PEMILIHAN MODEL REGRESI B-SPLINE TERBAIK MENGGUNAKAN METODE AKAIKE INFORMATION CRITERION (AIC) PADA DATA RUNTUN WAKTU (STUDI KASUS : NILAI TUKAR MATA UANG RUPIAH (IDR) TERHADAP DOLAR AMERIKA (USD) PERIODE JANUARI 2018 - JANUARI 2023)

Indriani Wahyu Nur Pratiwi, NIM.: 19106010049 (2023) PEMILIHAN MODEL REGRESI B-SPLINE TERBAIK MENGGUNAKAN METODE AKAIKE INFORMATION CRITERION (AIC) PADA DATA RUNTUN WAKTU (STUDI KASUS : NILAI TUKAR MATA UANG RUPIAH (IDR) TERHADAP DOLAR AMERIKA (USD) PERIODE JANUARI 2018 - JANUARI 2023). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Currency exchange rate data is time series data, which is a collection of data taken at the equally interval in time sequentially. One of the popular time series data modeling is Autoregressive Integrated Moving Average (ARIMA). ARIMA requires the assumption of stationarity, the assumption of normality, and the assumption of white noise that must be met as conditions to form the ARIMA model. If one of the assumptions is violated, B-spline regression can be an alternative. The purpose of this study is to construct the model of exchange rate of rupiah against the US dollar which can be used as a prediction of future exchange rates using B-spline regression. The optimal B-Spline regression model is obtained based on selecting the optimal combination of order and number of knot points using Akaike Information Criterion (AIC) optimization. Furthermore, an estimate of the model parameters was obtained and a model feasibility test was carried out to see the performance of the model using the Mean Absolute Percentage Error (MAPE) value. The combination of order and the optimal number of knot points is obtained in order 3 (quadratic) and 2 knot points with an AIC value of 857.8322 and a MAPE value of 0.0148376. Optimal models that obtained is: ˆy = 13526.08N−2,3(x) + 14177.5N−1,3(x) + 14145.24N0,3(x) + 15698.26N1,3(x) + 15156.4N2,3(x).

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