ANALISIS PERBANDINGAN MENGGUNAKAN ARIMA DAN BOOTSTRAP PADA PERAMALAN NILAI EKSPOR INDONESIA

Ari Cynthia; Sugiman Sugiman; Zaenuri Zaenuri

doi:10.15294/ujm.v5i1.13102

Davison, A.C. & D.V. Hinkley. 2006. Bootstrap Methods and Their Application. Cambridge University Press, Cambridge.
Efron, B & R.J. Tibshirani. 1998. An Introduction to the lkj Bootstrap. United States of America: CRC press LCC.
Karomah, Y. 2014. Estimasi Parameter Bootstrap pada Proses Arma dan Aplikasinya pada Harga Saham. Jurnal FMIPA, 4(1): 127.
Rahayu, S. & Tarno. 2006. Prediksi Produksi Jagung di Jawa Tengah dengan Arima dan Bootstrap. Prosiding SPMIPA. Semarang:nUniversitas Diponegoro.
Rosadi, D. 2011. Analisis Ekonometrika & Runtun Waktu Terapan dengan R. Yogyakarta: ANDI.
Sungkono, J. 2013. Resampling Bootstrap pada R. Artikel FKIP.
Suprihatin, B., S. Guritno, & S. Haryatmi. 2011. Estimasi Parameter Bootstrap pada Proses AR(1). Prosiding FMIPA. Semarang: Universitas Diponegoro.
Wei, W.W.S. 2006. Time Series Analysis Unvariate and Multivariate Methods Second Edition. United State of America: Addision-Wesley Publishing Company.

Abstract viewed - 1052 times
PDF downloaded - 1933 times

Affiliations

Ari Cynthia
Universitas Negeri Semarang

Sugiman Sugiman
Universitas Negeri Semarang

Zaenuri Zaenuri
Universitas Negeri Semarang

Content Top

ANALISIS PERBANDINGAN MENGGUNAKAN ARIMA DAN BOOTSTRAP PADA PERAMALAN NILAI EKSPOR INDONESIA

Ari Cynthia ,
Sugiman Sugiman ,
Zaenuri Zaenuri

Vol 5 No 1 (2016)

DOI 10.15294/ujm.v5i1.13102

Submitted: Feb 27, 2017

Published: Feb 27, 2017

Abstract

In this research used the data export value of Indonesia as a case study. Indonesia's export would be predicted using ARIMA and bootstrap methods with the help of the program R 2.11.1. Bootstrap method used is bootstrap the ARIMA process. ARIMA method is one of the most common methods used in modeling of time series. However on certain data time series models can not guarantee the fulfillment of the assumptions in the classical statistical analysis. Bootstrap methods can be used in situations where standard assumptions are not met. The main objective of this study is to compare the methods ARIMA and bootstrap the Indonesian export data so as to obtain the best forecasting method that will be used to forecast the data export value of Indonesia for the next period. Based on the results of the two models forecasting, it would have been the result of forecasting that has the smallest value of standard error and approach the original data. Results forecasting export value of Indonesia on ARIMA (1,1,2) has the smallest value and the standard error tends to approach the original data when compared to bootstrap the process models ARIMA (1,1,2). Then ARIMA method is the best forecasting method. Next will be forecasting for the months of April to December 2015 using ARIMA method as the best method.