APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG

APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG

Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling veh...

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Main Authors: Dian Anggraeni, Alan Prahutama, Shofi Andari
Format: Article
Language:English
Published: Universitas Diponegoro 2013-12-01
Series:Media Statistika
Online Access:https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639
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spelling doaj-485b24a1feb649a68d7ec4d5dbbcbcb82020-11-25T02:11:20ZengUniversitas DiponegoroMedia Statistika1979-36932477-06472013-12-0162617010.14710/medstat.6.2.61-706594APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANGDian AnggraeniAlan PrahutamaShofi AndariTime series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834. Key words: GSTAR, Vehicle Volume, Space-Time Modelhttps://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639
collection DOAJ
language English
format Article
sources DOAJ
author Dian Anggraeni
Alan Prahutama
Shofi Andari
spellingShingle Dian Anggraeni
Alan Prahutama
Shofi Andari
APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
Media Statistika
author_facet Dian Anggraeni
Alan Prahutama
Shofi Andari
author_sort Dian Anggraeni
title APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
title_short APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
title_full APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
title_fullStr APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
title_full_unstemmed APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
title_sort aplikasi generalized space time autoregressive (gstar) pada pemodelan volume kendaraan masuk tol semarang
publisher Universitas Diponegoro
series Media Statistika
issn 1979-3693
2477-0647
publishDate 2013-12-01
description Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834. Key words: GSTAR, Vehicle Volume, Space-Time Model
url https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639
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AT alanprahutama aplikasigeneralizedspacetimeautoregressivegstarpadapemodelanvolumekendaraanmasuktolsemarang
AT shofiandari aplikasigeneralizedspacetimeautoregressivegstarpadapemodelanvolumekendaraanmasuktolsemarang
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