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Multivariat Predict Sales Data Using the Recurrent Neural Network (RNN) Method

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 1 (2024) · pp. 83–94

DOI
10.22146/ijccs.90165
ISSN
2460-7258, 1978-1520
Published
2024-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Sales is an activity or business selling a product or service. In this study, I took a case study on Kaggle. Sales problems at the company cause inventory to be very high or vice versa, causing a loss of sales because there are no items to sell. Inventory that is too high results in increased costs due to existing resources being inefficient. In the opposite condition, it will cause a product vacancy in the market. Using the Recurrent Neural Network (RNN) Algorithm, this study predicts sales. The data used is sales data in 2020 with the parameter Number of sales per day in the last four months. The results obtained through testing several training scenarios and testing the implementation of the algorithm, in this case, is the highest accuracy value of 96.92% in the network architecture of three input neuron layers, three hidden layer neurons, one output, division of training, and test data 70: 30, learning value rate of 0.9 and a maximum of 9000000 epochs

Keywords

Collaboration network of the authors

6 authors · 12 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Bisnis dan Teknologi Indonesia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Putu SugiartawanInstitut Bisnis dan Teknologi Indonesia2

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Multivariat Predict Sales Data Using the Recurrent Neural Network (RNN) Method</dc:title>
	<dc:creator>Ardriani, Ni Nengah Dita</dc:creator>
	<dc:creator>Yastawil, Jamiin Al Yastawil</dc:creator>
	<dc:creator>Erawati, Kadek Nonik</dc:creator>
	<dc:creator>Yudi Antara, I Gede Made</dc:creator>
	<dc:creator>Santiago, Gede Agus</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Forecasting; Recurrent Neural Network; multivariate prediction</dc:subject>
	<dc:description xml:lang="en-US">Sales is an activity or business selling a product or service. In this study, I took a case study on Kaggle. Sales problems at the company cause inventory to be very high or vice versa, causing a loss of sales because there are no items to sell. Inventory that is too high results in increased costs due to existing resources being inefficient. In the opposite condition, it will cause a product vacancy in the market. Using the Recurrent Neural Network (RNN) Algorithm, this study predicts sales. The data used is sales data in 2020 with the parameter Number of sales per day in the last four months. The results obtained through testing several training scenarios and testing the implementation of the algorithm, in this case, is the highest accuracy value of 96.92% in the network architecture of three input neuron layers, three hidden layer neurons, one output, division of training, and test data 70: 30, learning value rate of 0.9 and a maximum of 9000000 epochs</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/90165</dc:identifier>
	<dc:identifier>10.22146/ijccs.90165</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 1 (2024): January; 83-94</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/90165/38506</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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