Sales Forecasting Model for Indonesian Clothing MSMEs For Sales Strategy Optimization Using The Long Short-Term Memory Method
DOI:
https://doi.org/10.52436/1.jutif.2026.7.1.5339Keywords:
Clothing Industry, LSTM, Machine Learning, MSMEs, Sales ForecastingAbstract
Micro, Small, and Medium Enterprises (MSMEs) in the clothing industry are one of the key pillars of the economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, MSMEs face considerable challenges related to market competition, shifting consumer trends, and fluctuating demand. Advances in data analytics and machine learning offer solutions to improve sales forecasting accuracy, thereby supporting more effective business strategies. This study aims to develop a sales forecasting model based on Long Short-Term Memory (LSTM) tailored to the characteristics of clothing MSMEs in Indonesia. The research was conducted at Ananda Kids MSME in Purbalingga, using 30,885 daily transaction records collected over 23 months. The dataset included product categories, sales volume, and revenue, which were further processed through normalization, handling of missing values, and the addition of seasonal features. The LSTM model was designed with 128 neurons and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the LSTM model achieved high accuracy for certain product categories. The “Set” and “Children’s Fashion” categories recorded MAPE values below 10%, demonstrating the model’s effectiveness in forecasting stable sales patterns. In contrast, categories with high volatility, such as accessories, produced larger prediction errors. These results highlight that data quality and sales pattern stability are crucial factors in enhancing model performance. Overall, the study demonstrates that the application of LSTM holds significant potential in supporting strategic decision-making for MSMEs through more accurate sales forecasting. Beyond its practical contributions for business actors, the study also provides a basis for the development of digitalization policies for the MSME sector in Indonesia.
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References
World Bank Group, “Capital Markets and SMEs in Emerging Markets and Developing Economies : Can They Go the Distance?,” 2020.
Kementerian Koperasi dan UKM Republik Indonesia, “PERKEMBANGAN DATA USAHA MIKRO, KECIL, MENENGAH (UMKM) DAN USAHA BESAR (UB) TAHUN 2018 - 2019,” 2019.
Q. Li and M. Yu, “Achieving Sales Forecasting with Higher Accuracy and Efficiency: A New Model Based on Modified Transformer,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 18, no. 4, pp. 1990–2006, Nov. 2023, doi: 10.3390/jtaer18040100.
P. Ganesan, “AI-Powered Sales Forecasting: Transforming Accuracy and Efficiency in Predictive Analytics,” Journal of Artificial Intelligence, Machine Learning and Data Science, vol. 2, no. 1, pp. 1213–1216, Feb. 2024, doi: 10.51219/JAIMLD/premkumar-ganesan/280.
X. Zhang, L. Zhang, Q. Zhou, and X. Jin, “A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–12, May 2022, doi: 10.1155/2022/1643413.
I. D. Mienye and T. G. Swart, “A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications,” Information, vol. 15, no. 12, p. 755, Nov. 2024, doi: 10.3390/info15120755.
M. Muth, M. Lingenfelder, and G. Nufer, “The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review,” Management Review Quarterly, Jun. 2024, doi: 10.1007/s11301-024-00447-8.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
J. Brownlee, Deep Learning for Time Series Forecasting Predict the Future with MLPs, CNNs and LSTMs in Python Acknowledgements Copyright Deep Learning for Time Series Forecasting, 1.4. 2018.
M. Waqas and U. W. Humphries, “A critical review of RNN and LSTM variants in hydrological time series predictions,” MethodsX, vol. 13, p. 102946, Dec. 2024, doi: 10.1016/j.mex.2024.102946.
A. John and T. Latha, “Stock market prediction based on deep hybrid RNN model and sentiment analysis,” Automatika, vol. 64, no. 4, pp. 981–995, Oct. 2023, doi: 10.1080/00051144.2023.2217602.
P. Jaquart, S. Köpke, and C. Weinhardt, “Machine learning for cryptocurrency market prediction and trading,” The Journal of Finance and Data Science, vol. 8, pp. 331–352, Nov. 2022, doi: 10.1016/j.jfds.2022.12.001.
Z.-C. Huang, I. Sangiorgi, and A. Urquhart, “Forecasting Bitcoin volatility using machine learning techniques,” Journal of International Financial Markets, Institutions and Money, vol. 97, p. 102064, Dec. 2024, doi: 10.1016/j.intfin.2024.102064.
R. F. Inaku and J. C. Chandra, “IMPLEMENTASI DATA MINING DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM),” Jurnal Ticom: Technology of Information and Communication, vol. 12, no. 1, pp. 1–7, Sep. 2023, doi: 10.70309/ticom.v12i1.99.
Y.-S. Shih and M.-H. Lin, “A LSTM Approach for Sales Forecasting of Goods with Short-Term Demands in E-Commerce,” 2019, pp. 244–256. doi: 10.1007/978-3-030-14799-0_21.
V. Shah, “A Comparative Study of Univariate Time-series Methods for Sales Forecasting,” University of Waterloo, Canada, 2019.
N. Nassibi, H. Fasihuddin, and L. Hsairi, “Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, 2023, doi: 10.14569/IJACSA.2023.01403101.
T.-M. Choi, C.-L. Hui, and Y. Yu, “Intelligent time series fast forecasting for fashion sales: A research agenda,” in 2011 International Conference on Machine Learning and Cybernetics, IEEE, Jul. 2011, pp. 1010–1014. doi: 10.1109/ICMLC.2011.6016870.
I. Ritharson. P, J. Anitha, and S. I. Alex Pandian, “A Web-based Real-time Sales Data Analysis using LSTM Model for Better Insight,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2023, pp. 1060–1064. doi: 10.1109/ICACCS57279.2023.10112837.
T. de Castro Moraes, X. Yuan, and E. P. Chew, “Hybrid convolutional long short‐term memory models for sales forecasting in retail,” Journal of Forecasting, vol. 43, no. 5, pp. 1278–1293, Aug. 2024, doi: 10.1002/for.3073.
F. Firmansyah, A. Fatharani, and U. Anis, “Forecasting Using the Time Series Forecasting Model at SMEs Pempek Dang Tirta, Banyumas, Central Java,” Jurnal Teknologi Agro-Industri, vol. 11, no. 2, pp. 91–103, Nov. 2024, doi: 10.34128/jtai.v11i2.199.
I. P. Tama, W. S. Nugroho, W. F. Mahmudy, and P. Purnami, “The Evaluation of Technology Startup Role on Indonesian SMEs Industry 4.0 Adoption Using CLD-ABM Integrated Model,” Sustainability, vol. 14, no. 14, p. 8462, Jul. 2022, doi: 10.3390/su14148462.
R. Sijabat, “The Effects of Business Digitalization and Knowledge Management Practices on Business Performance: Findings from Indonesian Micro, Small, and Medium Enterprises,” BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi, vol. 29, no. 2, May 2022, doi: 10.20476/jbb.v29i2.1350.
A. B. Raya et al., “Challenges, Open Innovation, and Engagement Theory at Craft SMEs: Evidence from Indonesian Batik,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 7, no. 2, p. 121, Jun. 2021, doi: 10.3390/joitmc7020121.
M. I. Fawzi, Transformasi Digital: Membangkitkan Kinerja Bisnis di Tengah Pandemi dengan Adopsi TI, 1st ed. Banyumas: PT. Pena Persada Kerta Utama, 2023.
M. I. Fawzi and A. P. Subriadi, “Dampak Adopsi TI Terhadap Kinerja Perusahaan Multinasional,” JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN), vol. 8, no. 2, pp. 215–227, May 2022, doi: 10.31289/jkbm.v8i2.7320.
M. I. Fawzi and A. P. Subriadi, “The impact of IT adoption toward multinational enterprise’s performance: A systematic literature review,” in AIP Conference Proceedings, American Institute of Physics Inc., Feb. 2023. doi: 10.1063/5.0110376.
Meisenbacher et al., “A Review of Automated Time Series Forecasting Pipelines,” Business & Information Systems Engineering, 2022.
N. Koliou et al., “Comparing Prior and Learned Time Representations in Deep Learning Models,” ACM Transactions on Knowledge Discovery from Data, 2024.
C. D. Lewis, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. London: Butterworth Scientific, 1982.
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Copyright (c) 2026 Muhammad Ihsan Fawzi, Laurensia Claudia Pratomo, Dian Isnawati, Nur Chasanah, Nadhifa Zahra Kurniawan
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