BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting

Maria, E; Wahyono, T; Hartomo, KD; Purwanto,; Arthur, C

BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting

Maria, E Wahyono, T Hartomo, KD Purwanto, Arthur, C

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Publisher:: Institute of Advanced Engineering and Science
Publication Type:: Journal Article
Citation:: Bulletin of Electrical Engineering and Informatics, 2025, 14, (3), pp. 2004-2016
Issue Date:: 2025-06-01

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Field	Value	Language
dc.contributor.author	Maria, E
dc.contributor.author	Wahyono, T
dc.contributor.author	Hartomo, KD
dc.contributor.author	Purwanto,
dc.contributor.author	Arthur, C
dc.date.accessioned	2026-06-23T06:25:44Z
dc.date.available	2026-06-23T06:25:44Z
dc.date.issued	2025-06-01
dc.identifier.citation	Bulletin of Electrical Engineering and Informatics, 2025, 14, (3), pp. 2004-2016
dc.identifier.issn	2089-3191
dc.identifier.issn	2302-9285
dc.identifier.uri	http://hdl.handle.net/10453/195456
dc.description.abstract	This paper presents bidirectional long short-term memory (BiLSTM) OptiFlow, an optimized deep learning model designed to predict the financial health of cooperatives using key financial ratios: debt to equity ratio (DER), net profit margin (NPM), and return on equity (ROE). By leveraging a BiLSTM architecture combined with an optimal decayed learning rate, this model aims to enhance forecasting accuracy. The proposed model was tested against three established methods—recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—and evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) metrics. Results indicate that BiLSTM OptiFlow outperforms the other models across all key indicators. This research offers a robust approach to cooperative financial forecasting, with significant implications for decision-making processes in cooperative management.
dc.language	en
dc.publisher	Institute of Advanced Engineering and Science
dc.relation.ispartof	Bulletin of Electrical Engineering and Informatics
dc.relation.isbasedon	10.11591/eei.v14i3.8653
dc.rights	info:eu-repo/semantics/openAccess
dc.title	BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting
dc.type	Journal Article
utslib.citation.volume	14
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/
dc.date.updated	2026-06-23T06:25:43Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	3

Abstract:

This paper presents bidirectional long short-term memory (BiLSTM) OptiFlow, an optimized deep learning model designed to predict the financial health of cooperatives using key financial ratios: debt to equity ratio (DER), net profit margin (NPM), and return on equity (ROE). By leveraging a BiLSTM architecture combined with an optimal decayed learning rate, this model aims to enhance forecasting accuracy. The proposed model was tested against three established methods—recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—and evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) metrics. Results indicate that BiLSTM OptiFlow outperforms the other models across all key indicators. This research offers a robust approach to cooperative financial forecasting, with significant implications for decision-making processes in cooperative management.

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http://hdl.handle.net/10453/195456