Machine Learning Approaches for Predicting Seasonal Stock Trends

Gunawan, Jason Miracle; Andreas, Christopher; Saputri, Theresia Ratih Dewi

doi:10.22146/ijccs.112504

Open access Jurnal SINTA 2

Machine Learning Approaches for Predicting Seasonal Stock Trends

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 4 (2025) · pp. 441–450

DOI: 10.22146/ijccs.112504
ISSN: 2460-7258, 1978-1520
Published: 2025-10-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors

Keywords

Collaboration network of the authors

12 authors · 28 co-authorship links · 19 across different organisations

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

Universitas Ciputra
Universitas Airlangga
Institut Teknologi Telkom Surabaya
Emerge
Padjadjaran University
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Bryan Samuel	Universitas Ciputra	1
Angela Melia Gunawan	Affiliation unknown	1
Sharon Tan	Affiliation unknown	1
Muhammad Achirul Nanda	Padjadjaran University	1
Anifatul Faricha	Institut Teknologi Telkom Surabaya	1
Siti Maghfirotul Ulyah	Universitas Airlangga	1
Rika Susanti	Emerge	1
Hawwin Mardhiana	Institut Teknologi Telkom Surabaya	1
Ilma Amira Rahmayanti	Universitas Airlangga	1

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">Machine Learning Approaches for Predicting Seasonal Stock Trends</dc:title>
	<dc:creator>Gunawan, Jason Miracle</dc:creator>
	<dc:creator>Andreas, Christopher</dc:creator>
	<dc:creator>Saputri, Theresia Ratih Dewi</dc:creator>
	<dc:subject xml:lang="en-US">computer science</dc:subject>
	<dc:subject xml:lang="en-US">machine learning; Gated Recurrent Unit; Stock Price Forecasting; Seasonal Pattern Analysis; Indonesia Transportation Sector</dc:subject>
	<dc:description xml:lang="en-US">The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
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	<dc:date>2025-10-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/112504</dc:identifier>
	<dc:identifier>10.22146/ijccs.112504</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 4 (2025): October; 441-450</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/112504/42794</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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