Peramalan Jumlah Uang Beredar dengan Pendekatan Model Arimax dan Autoregressive Distributed Lag (Ardl).

Rahma, Awanda Yunita (2022) Peramalan Jumlah Uang Beredar dengan Pendekatan Model Arimax dan Autoregressive Distributed Lag (Ardl). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kestabilan ekonomi menjadi kunci utama keberhasilan suatu negara salah satunya Indonesia. Bank Indonesia (BI) mempunyai tujuan dalam mencapai dan menjaga kestabilan ekonomi melalui identifikasi kondisi jumlah uang beredar di periode ke depan sehingga mampu menekan laju inflasi yang terjadi. Tujuan penelitian ini diharapkan mampu meramalkan jumlah uang yang beredar menggunakan ARIMA, ARIMAX, dan ARDL dan didapatkan bahwa peredaran uang diketahui terdapat pola musiman pada data ini. Pemodelan ARIMAX dengan variasi kalender dan variabel IHK dan Kurs didapatkan model signifikan ARIMA (1,1,0) dengan residual white noise dan normal. Pemodelan ARIMAX dengan efek fungsi transfer berupa IHK didapatkan model signifikan $ARIMA(0,1,[1,6,12])(0,1,0)^{6}$ dengan residual white noise dan diasumsikan normal setelah dilakukan deteksi outlier. Pemodelan ARIMAX dengan efek fungsi transfer berupa Kurs didapatkan model signifikan $ARIMA(4,1,0)(1,0,0)^{12}$ dengan residual white noise dan diasumsikan normal. Pemodelan ARDL dengan IHK didapatkan model signifikan dengan melibatkan $IHK_{t-1}$, $IHK_{t-2}$, dan $Z_{t-6}$ dengan residual diasumsikan white noise dan telah normal. Pemodelan ARDL dengan Kurs didapatkan model signifikan dengan melibatkan Kurst, $Z_{t-6}$, dan $Z_{t-7}$ dengan residual diasumsikan white noise dan normal. Seluruh kebaikan model dari masing-masing metode didapatkan bahwa pemodelan ARIMAX dengan variasi kalender dan variabel IHK dan Kurs yang terbaik karena memenuhi kedua asumsi yaitu residual white noise dan normal.
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Economic stability is the main key to the success of a country, one of which is Indonesia. Bank Indonesia (BI) has a goal in achieving and maintaining economic stability through identifying the condition of the money supply in the future period so as to be able to suppress the inflation rate that occurs. The purpose of this study is expected to be able to predict the amount of money in circulation using ARIMA, ARIMAX, and ARDL and it is found that the circulation of money is known to have seasonal patterns in this data. ARIMAX modeling with calendar variations and CPI and exchange rate variables obtained a significant ARIMA model (1,1,0) with residual white noise and normal. ARIMAX modeling with the transfer function effect in the form of CPI obtained a significant model $ARIMA(0,1,[1,6,12])(0,1,0)6$ with residual white noise and assumed to be normal after outlier detection. ARIMAX modeling with the transfer function effect in the form of exchange rate obtained a significant model ARIMA(4,1,0)(1,0,0)12 with residual white noise and assumed to be normal. ARDL modeling with CPI obtained a significant model involving IHKt-1, IHKt-2, and Zt-6 with residuals assumed to be white noise and normal. ARDL modeling with Kurs obtained a significant model involving Kurst, Zt-6, and Zt-7 with residuals assumed to be white noise and normal. The whole goodness of the model from each method is found that the determination of the use of the model depends on the case to be solved.

Item Type:	Thesis (Other)
Additional Information:	RSSt 519.535 Rah p-1 2022
Uncontrolled Keywords:	ARDL. ARIMAX. IHK. Uang Beredar. Peramalan. ARDL. ARIMAX. Forecasting. IHK. Money Circulation.
Subjects:	H Social Sciences > HA Statistics
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	10 Jun 2026 01:21
Last Modified:	10 Jun 2026 03:26
URI:	http://repository.its.ac.id/id/eprint/133665

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