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Jurnal SINTA 2

PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 7 No. 2 (2008) · hlm. 107–114

DOI
10.12962/j24068535.v7i2.a180
ISSN
2406-8535, 1412-6389
Terbit
2008-07-01
Penerbit
Institut Teknologi Sepuluh Nopember
Bahasa
eng

Abstrak

Forecasting technique is an important component of decision making because it aims to predict values of data in the future. Many existing forecasting methods only predict single variable data and thus do not consider correlation between variable in a dataset. This paper proposes Vector Autoregressive Moving Average (VARMA) as a forecasting method to predict data with more than one variable. This method combines regression concept i.e. autoregressive (VAR) and moving average method (VMA) for multi-variables data. The first step in VARMA method is testing the stationary of the data. Differencing process is conducted in order to change non stationary data to stationary. The next step is to identify the order of the VARMA model of the stationary data. The parameters are then estimated according to the order and co-integration test is conducted on the variables. The model is tested to assess its validity. If the model is valid then forecasting can be done using the model generic formula. The errors of the forecast are calculated to evaluate the performance of the model. Random values are found in the forecast of VARMA method. However, the error remains within a certain interval. The error interval is below 10% so it can be argued that this model is very accurate in predicting the data. Keywords: Forecasting, Multi-Variables, VAR, VMA, VARM, Co-integration Test

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18 penulis · 58 tautan kepenulisan bersama · 7 lintas organisasi

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  • Institut Teknologi Sepuluh Nopember
  • Universitas Muhammadiyah Malang
  • Universitas Katolik Widya Mandira
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Rekan penulisOrganisasiArtikel bersama
Edwin RiksakomaraInstitut Teknologi Sepuluh Nopember2
Handayani TjandrasaAfiliasi tidak diketahui1
Febriliyan SamopaInstitut Teknologi Sepuluh Nopember1
Radityo Prasetianto WibowoInstitut Teknologi Sepuluh Nopember1
Tresnaning ArifiyahInstitut Teknologi Sepuluh Nopember1
Pujiadi PujiadiAfiliasi tidak diketahui1
Siti Aminatus ZehrohInstitut Teknologi Sepuluh Nopember1
Nur Aini LestariInstitut Teknologi Sepuluh Nopember1
Yeyen SudiartiInstitut Teknologi Sepuluh Nopember1
Muhammad Ilham PerdanaUniversitas Muhammadiyah Malang1
Adri Gabriel SooaiUniversitas Katolik Widya Mandira1
Retno Aulia VinartiInstitut Teknologi Sepuluh Nopember1
Ahmad MuklasonInstitut Teknologi Sepuluh Nopember1
Faizal MahanantoInstitut Teknologi Sepuluh Nopember1
Raras TyasnuritaInstitut Teknologi Sepuluh Nopember1
Mursyidatun NabilahInstitut Teknologi Sepuluh Nopember1

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Sumber metadata: OAI-PMH (metadata jurnal) https://juti.if.its.ac.id/index.php/juti/oai, pertama di-harvest 26 Okt 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)</dc:title>
	<dc:creator xml:lang="en">Anggraeni, Wiwik</dc:creator>
	<dc:creator xml:lang="en">Dewi, Leivina Kartika</dc:creator>
	<dc:description xml:lang="en">   Forecasting technique is an important component of decision making because it aims to predict values of data in the future. Many existing forecasting methods only predict single variable data and thus do not consider correlation between variable in a dataset. This paper proposes Vector Autoregressive Moving Average (VARMA) as a forecasting method to predict data with more than one variable. This method combines regression concept i.e. autoregressive (VAR) and moving average method (VMA) for multi-variables data. The first step in VARMA method is testing the stationary of the data. Differencing process is conducted in order to change non stationary data to stationary. The next step is to identify the order of the VARMA model of the stationary data. The parameters are then estimated according to the order and co-integration test is conducted on the variables. The model is tested to assess its validity. If the model is valid then forecasting can be done using the model generic formula. The errors of the forecast are calculated to evaluate the performance of the model. Random values are found in the forecast of VARMA method. However, the error remains within a certain interval. The error interval is below 10% so it can be argued that this model is very accurate in predicting the data.   Keywords: Forecasting, Multi-Variables, VAR, VMA, VARM, Co-integration Test</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2008-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/180</dc:identifier>
	<dc:identifier>10.12962/j24068535.v7i2.a180</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 7, No 2, Juli 2008; 107-114</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/180/135</dc:relation>
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