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ITS » Paper and Presentation » Statistika
Posted by fandikaaqsa@its.ac.id at 11/04/2016 14:13:35  •  1101 Views


FORECASTING NETFLOW OF MONEY CURRENCY USING ARIMAX AND RADIAL BASIS FUNCTION NETWORK METHODS CASE STUDY IN BANK INDONESIA

PERAMALAN NETFLOW UANG KARTAL DENGAN METODE ARIMAX DAN RADIAL BASIS FUNCTION NETWORK STUDI KASUS DI BANK INDONESIA

Author :
WULANSARI, RENNY ELFIRA ( 1310100022 )




ABSTRAK

Bank Indonesia BI merupakan bank sentral Republik Indonesia. BI memiliki satu tujuan tunggal yakni mencapai dan menjaga kestabilan nilai rupiah. Salah satu hal yang dilakukan untuk memenuhi tujuan ini adalah dengan pemantauan netflow uang kartal agar BI dapat menentukan kebijakan terhadap proses uang keluar dan uang masuk pada BI. Pemantauan ini dilakukan lewat peramalan nilai netflow uang kartal. Metode peramalan pada BI yang masih menggunakan ARIMA dan ekstrapolasi data belum maksimal dalam meramalkan netflow uang kartal. Maka dari itu pada penelitian ini netflow uang kartal akan diramalkan dengan metode yang berbeda yakni ARIMAX dan Artificial Neural Network ANN. Dari kedua metode ini akan dibandingkan hasil peramalan metode mana yang lebih baik. ARIMAX yang digunakan adalah ARIMAX dengan efek variasi kalender dan variabel prediktor Indeks Harga Konsumen IHK serta kurs. ARIMAX dengan efek variasi kalender digunakan karena diketahui adanya hari raya idul fitri memperngaruhi netflow uang kartal. Sedangkan metode ANN yang digunakan adalah Radial Basis Function Network RBFN. Ini karena metode ANN sebagai teknik peramalan baru dalam bidang ekonomi dan keuangan pada beberapa penelitian hasil peramalannya lebih unggul dibanding metode yang ada sebelumnya Periode data yang digunakan pada penelitian ini adalah Januari 2005 hingga Desember 2013. Diperoleh hasil bahwa model ARIMAX dengan efek variasi kalender dan variabel prediktor IHK merupakan model dengan peramalan netflow uang kartal terbaik dibanding model-model lain pada penelitian ini.


ABSTRACT

Bank Indonesia BI is the central bank of the Republic of Indonesia. BI has one single overarching objective to establish and maintain rupiah stability. One of the things which has done to achieve this goal is by monitoring netflow of money currency so BI can decide policy toward the money which is out and in to BI. This monitoring is done through forecasting from netflow of money currency value. Forecasting methods in BI are still using ARIMA and the extrapolation of data that is not maximized in predicting netflow of money currency. Thus in this study netflow of money currency would be predicted by different methods namely ARIMAX and Artificial Neural Network ANN. Of both methods will be compared to the results of forecasting which method is better. ARIMAX which is used is ARIMAX with calendar variation effect the predictor variables Consumer Price Index CPI and exchange rate. ARIMAX with calendar variation effect is used because Eid-holidays affect netflow of money currency. While the ANN method used is Radial Basis Function Network RBFN. It is caused the ANN method as a new forecasting techniques in the field of economics and finance at some research gave better forecasting results than existing methods. Period data used in this study is January 2005 to December 2013. The result shows that ARIMAX model with calendar variation effect and CPI as a predictor is the best model for forecasting netflow of money currency.



KeywordsNetflow uang kwartal; IHK; kurs; peramalan; variasi kalender; fungsi transfer; RBFN
 
Subject:  Statistik
Contributor
  1. Dr. Suhartono, S.Si., M.Sc.
Date Create: 11/04/2016
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-paper-13021160007095
Collection ID: 13021160007095
Call Number: RSSt 511.326 Wul p


Source
Paper and Presentations of Statistics, RSSt 511.326 Wul p, 2014

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Copyright @2016 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-paper-41052-1310100022-paper.pdf - 808 KB
  2.  ITS-paper-41052-1310100022-presentation.pdf - 3435 KB




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