Peramalan Arus Uang Harian Di kantor Pusat Bank Indonesia Untuk Perencanaan Peredaran Uang Kartal Dengan Metode Regresi Time Series Dan ARIMAX Variasi Kalender - Forecasting Of Daily Cash Flow In Bank Indonesia For Planning Currency Circulation Using Time Series Regression And Calender Variation Arimax Method

Sari, Hanna Kartika (2015) Peramalan Arus Uang Harian Di kantor Pusat Bank Indonesia Untuk Perencanaan Peredaran Uang Kartal Dengan Metode Regresi Time Series Dan ARIMAX Variasi Kalender - Forecasting Of Daily Cash Flow In Bank Indonesia For Planning Currency Circulation Using Time Series Regression And Calender Variation Arimax Method. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Bank Indonesia merupakan otoritas moneter yang mempunyai tugas menetapkan dan melaksanakan kebijakan moneter, dengan mengendalikan jumlah uang beredar. Ketersediaan uang rupiah layak edar tercermin oleh jumlah dan laju pertumbuhan uang kartal yang diedarkan (UYD) maupun aliran uang kartal yang keluar dari Bank Indonesia ke perbankan dan masyarakat (outflow) dan aliran uang kartal yang masuk melalui Bank Indonesia (inflow). Kebutuhan uang kartal di Indonesia cenderung meningkat di hari tertentu seperti hari raya Idul Fitri. Sehingga jauh-jauh hari Bank Indonesia mengantisipasi hal tersebut dengan cara meramalkan. Pada penelitian ini dilakukan peramalan arus uang harian di Kantor Pusat Bank Indonesia dengan metode Regresi Time Series serta ARIMAX Variasi Kalender Multi Input dan Single Input. Data yang digunakan adalah data inflow dan outflow harian serta dummy variasi kalender. Hasil analisis menunjukkan bahwa rata-rata inflow uang kartal cenderung meningkat pada hari Selasa dan Rabu, Minggu kedua, bulan Agustus. Sedangkan outflow meningkat pada Kamis dan Jum’at, Minggu keempat, bulan Juli. Metode regresi time series merupakan metode terbaik untuk meramalkan inflow dan outflow. Berdasarkan hasil RMSE terkecil yang dibandingkan dengan ketiga metode, didapatkan model 1 regresi time series baik untuk meramalkan inflow 2015 sedangkan model 3 regresi time series baik untuk meramalkan outflow 2015. Hal ini menunjukkan bahwa model yang komplek tidak selalu menghasilkan ramalan yang lebih akurat dibanding model yang sederhana. ======================================================================================================================== Bank Indonesia is the monetary authority has the task that define and implement monetary policy by controlling the money supply. Availability of money circulation is reflected by the amount and rate of growth of currency in circulation (UYD) as well as the flow of currency out of a Bank Indonesia to banks and the public (outflow) and the flow of currency that goes through Bank Indonesia (inflow). The need for currency in Indonesia tends to increase at certain days such as Eid. So far the Bank Indonesia anticipate this by way of predicting. In this research, did forecasting daily cash flow at Head Office of Bank Indonesia using Time Series Regression and Calendar Variation ARIMAX Multi Input and Single Input. Data for this research is daily inflow and outflow and calendar variations dummy. The result of analysis showed that the average inflow of currency tends to rise on Tuesday and Wednesday, the second week, the month of August. While outflows increased on Thursday and Friday, the fourth Sunday in July. Time series regression method is the best method to forecast the inflow and outflow. Based on the results of the smallest RMSE compared with the three methods, it was found 1 time series regression models to predict both inflow in 2015 while three time series regression models to predict both outflow 2015. This indicates that the model is complex does not always produce more accurate predictions than the simple model.

Item Type: Thesis (Undergraduate)
Additional Information: RSSt 511.526 Sar p
Uncontrolled Keywords: ARIMAX Variasi Kalender, Inflow, Outflow, Regresi Time Series, Uang Kartal, Calendar Variation ARIMAX, Currency, Inflow, Outflow, Time Series Regression.
Subjects: H Social Sciences > HA Statistics > HA30.3 Time-series analysis
Divisions: Faculty of Mathematics and Science > Statistics
Depositing User: ansi aflacha
Date Deposited: 15 Nov 2019 06:52
Last Modified: 15 Nov 2019 06:52
URI: http://repository.its.ac.id/id/eprint/71822

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