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INFLATION FORECASTS IN AMBON USING NEURAL NETWORK APPLICATIONS BACKPROPAGATION

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 2 (2022) · pp. 483–496

DOI
10.30598/barekengvol16iss2pp483-496
ISSN
2615-3017, 1978-7227
Published
2022-06-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

An artificial Neural Network is the processing of information systems on certain characteristics which are artificial representations based on human neural networks. Artificial Neural Networks can be applied to various fields in human life, one of which is the economic field. In this study, the Artificial Neural Network is used to predict the inflation rate using the Backpropagation method. The data used in this study is 144 data, with 100 data as training data and 44 data as test data taken from the Central Statistics Agency of Maluku Province from 2008-2019. The best prediction accuracy level is obtained by using learning rate (a) = 0.1, Target Error = 0.000001, Maximum epoch = 500, network architecture 11-1, and 70% training data sharing scheme and 30% test data. The average absolute error percentage (MAPE) is 85.21%.

Collaboration network of the authors

40 authors · 83 co-authorship links · showing the 40 most connected

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Co-authorOrganisationJoint articles
Yopi Andry LesnussaAffiliation unknown5
Yopi A. LesnussaAffiliation unknown4
Henry J. WattimanelaAffiliation unknown2
Marlon S. N. van DelsenAffiliation unknown2
Henry W.M. PattyAffiliation unknown2
Lexy J. SinayAffiliation unknown2
Mozart Winston TalakuaAffiliation unknown2
Zeth A. LeleuryAffiliation unknown2
Dorteus L. RahakbauwAffiliation unknown2
Stenly J. NanuruAffiliation unknown1
Venn Y. I. IlwaruAffiliation unknown1
Francis Y. RumlawangAffiliation unknown1
Jefri TipkaAffiliation unknown1
Lidia SalakaAffiliation unknown1
Trifena L. WasilaineAffiliation unknown1
F. S. TututparyAffiliation unknown1
Melky M. RomseryAffiliation unknown1
Sitri YuniAffiliation unknown1
HARMANUS BATKUNDEAffiliation unknown1
M. TomasoaAffiliation unknown1
Geornando G. PattyAffiliation unknown1
Ferry Kondo LembangAffiliation unknown1
Angelia C. LeunupunAffiliation unknown1
Serly SurlialyAffiliation unknown1
Abraham Z. WattimenaAffiliation unknown1
Vanbasten TemartenanAffiliation unknown1
A. W. TalutaAffiliation unknown1
Salmon N. AuleleAffiliation unknown1
B. TuasikalAffiliation unknown1
H. AbrahamsAffiliation unknown1
Jeky TetimelayAffiliation unknown1
Jemsr S. BatlajeryAffiliation unknown1
Salmon Noce AuleleAffiliation unknown1
Helda Y. TaihuttuAffiliation unknown1
DORTEUS LODEWYIK RAHAKBAUWAffiliation unknown1
Muh. Iskandar TanassyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">INFLATION FORECASTS IN AMBON USING NEURAL NETWORK APPLICATIONS BACKPROPAGATION</dc:title>
	<dc:creator>Talakua, Mozart W</dc:creator>
	<dc:creator>Ilwaru, Venn Yan Ishak</dc:creator>
	<dc:creator>Tomasouw, Berny P</dc:creator>
	<dc:creator>Limba, Syella Z</dc:creator>
	<dc:description xml:lang="en-US">An artificial Neural Network is the processing of information systems on certain characteristics which are artificial representations based on human neural networks. Artificial Neural Networks can be applied to various fields in human life, one of which is the economic field. In this study, the Artificial Neural Network is used to predict the inflation rate using the Backpropagation method. The data used in this study is 144 data, with 100 data as training data and 44 data as test data taken from the Central Statistics Agency of Maluku Province from 2008-2019. The best prediction accuracy level is obtained by using learning rate (a) = 0.1, Target Error = 0.000001, Maximum epoch = 500, network architecture 11-1, and 70% training data sharing scheme and 30% test data. The average absolute error percentage (MAPE) is 85.21%.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5019</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss2pp483-496</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 483-496</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss2year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5019/4476</dc:relation>
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