Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction

Sugiartawan, Putu; Saputra, Yusril Eka; Munir, Agus Qomaruddin

doi:10.22146/ijccs.90149

Open access Jurnal SINTA 2

Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 417–428

DOI: 10.22146/ijccs.90149
ISSN: 2460-7258, 1978-1520
Published: 2023-10-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.

Keywords

Collaboration network of the authors

9 authors · 24 co-authorship links · 20 across different organisations

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

Mulawarman University
Institut Bisnis dan Teknologi Indonesia
Universitas Respati Yogyakarta
Musamus Merauke University
STMIK Banjarbaru
STMIK El Rahma Yogyakarta

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Anindita Septiarini	Mulawarman University	2
Hamdani Hamdani	Mulawarman University	2
Budi Rahmani	STMIK Banjarbaru	2
Heru Ismanto	Musamus Merauke University	2
Andri Syafrianto	STMIK El Rahma Yogyakarta	2
Didit Suprihanto	Mulawarman University	2

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">Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction</dc:title>
	<dc:creator>Sugiartawan, Putu</dc:creator>
	<dc:creator>Saputra, Yusril Eka</dc:creator>
	<dc:creator>Munir, Agus Qomaruddin</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Prediction; Retail Business; Convolutional Long Short-Term Memory</dc:subject>
	<dc:description xml:lang="en-US">The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.</dc:description>
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	<dc:date>2023-10-31</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 417-428</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
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	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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