Lastiur, Sunrie Kristella (2026) Pemodelan Volatilitas dan Forecasting Nilai Kurs USD/IDR Menggunakan Metode ARIMA-GARCH. Bachelor thesis, Institut Teknologi Kalimantan.
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Abstract
Nilai tukar USD/IDR merupakan indikator ekonomi makro yang penting karena mencerminkan stabilitas ekonomi dan memengaruhi pengambilan keputusan di berbagai sektor. Pergerakan nilai tukar bersifat fluktuatif dan sering menunjukkan volatilitas yang berubah-ubah dari waktu ke waktu, sehingga diperlukan metode peramalan yang mampu memodelkan karakteristik rata-rata dan varians secara simultan. Penelitian ini bertujuan untuk menentukan model ARIMA terbaik, memodelkan volatilitas menggunakan GARCH, serta melakukan peramalan nilai tukar USD/IDR dan mengevaluasi kinerja model. Data yang digunakan merupakan data harian nilai tukar USD/IDR periode April 2022 hingga Desember 2025 sebanyak 958 observasi yang diperoleh dari situs investing.com, dengan pembagian 80% data training dan 20% data testing. Metodologi penelitian diawali dengan preprocessing data, uji stasioneritas varians menggunakan transformasi Box-Cox, uji stasioneritas mean menggunakan uji Augmented Dickey-Fuller (ADF), identifikasi model melalui plot ACF dan PACF, estimasi dan seleksi model ARIMA terbaik berdasarkan signifikansi parameter, uji white noise, dan AIC, dilanjutkan dengan uji heteroskedastisitas ARCH-LM, estimasi model GARCH, peramalan, serta evaluasi kinerja. Hasil penelitian menunjukkan bahwa uji Box-Cox menghasilkan λ = 0,8821 dengan confidence interval 95% = [−1,1052 ; 2,8796] yang mencakup nilai 1, sehingga data tidak memerlukan transformasi. Model ARIMA terbaik yang diperoleh adalah ARIMA(0,1,1) dengan AIC = 8.237,4444. Uji ARCH-LM menghasilkan p-value = 0,0034 yang membuktikan keberadaan efek ARCH sehingga model GARCH diperlukan. Model GARCH terbaik adalah ARCH (1) dengan AIC = 8.333,5046, sehingga model final yang digunakan adalah ARIMA(0,1,1)-ARCH(1). Evaluasi kinerja model menghasilkan RMSE = 51,0140, MAD = 33,6146, dan MAPE = 0,2039% yang termasuk dalam kategori sangat baik.
| Item Type: | Thesis (Bachelor) |
|---|---|
| Subjects: | A General Works > AI Indexes (General) |
| Divisions: | Jurusan Matematika dan Teknologi Informasi > Statistik |
| Depositing User: | Sunrie Kristella Lastiur |
| Date Deposited: | 17 Jul 2026 05:51 |
| Last Modified: | 17 Jul 2026 05:51 |
| URI: | http://repository.itk.ac.id/id/eprint/27011 |
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