ANALISIS PREDIKSI PENJUALAN TOKO FURNITUR DENGAN METODE LONG SHORT-TERM MEMORY (LSTM)
Abstract
This study aims to analyze and predict furniture store sales using the Long Short-Term Memory (LSTM) method, focusing on time series datasets from 2014 to 2017. The LSTM method was chosen because of its ability to handle remote dependencies in time series data, which is relevant in understanding furniture sales patterns and trends for strategic planning. The research stages include literature study, library research, field research, data acquisition, and data preprocessing using Python and Google Colab. Exploratory analysis of the data was carried out to understand the characteristics of the dataset, followed by the development of the LSTM model, data normalization, and model evaluation with RMSE, MAE, and MAPE metrics. The evaluation results show that the LSTM model produces RMSE of 39.27%, MAE of 32.74%, and MAPE of 42.28%. Nonetheless, there is potential to improve accuracy by integrating more variables, exploring different LSTM architectures, and utilizing regularization techniques. This research is expected to contribute to improving the effectiveness of furniture sales management strategies and inspire further development in the application of ESG in business prediction.
References
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[2] H. A. Dau et al., “The UCR time series archive,” IEEE/CAA J. Autom. Sin., vol. 6, no. 6, pp. 1293–1305, 2019, doi: 10.1109/JAS.2019.1911747.
[3] M. Friedman, “The Interpolation of Time Series by Related Series,” J. Am. Stat. Assoc., vol. 57, no. 300, pp. 729–757, 1962, doi: 10.1080/01621459.1962.10500812.
[4] P. Moriarty, “Modern Methods of Prediction,” Encyclopedia, vol. 3, no. 2, pp. 520–529, 2023, doi: 10.3390/encyclopedia3020037.
[5] K. Gao, Z. Zhou, and Y. Qin, “Gas concentration prediction by LSTM network combined with wavelet thresholding denoising and phase space reconstruction,” Heliyon, vol. 10, no. 7, p. e28112, 2024, doi: 10.1016/j.heliyon.2024.e28112.
[6] Z. Peng et al., “A novel deep learning ensemble model with data denoising for short-term wind speed forecasting,” Energy Convers. Manag., vol. 207, no. October 2019, p. 112524, 2020, doi: 10.1016/j.enconman.2020.112524.
[7] J. Shi, R. Myana, V. Stebliankin, A. Shirali, and G. Narasimhan, “Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 14343 LNAI, pp. 56–75, 2023, doi: 10.1007/978-3-031-49896-1_5.
[8] M. Wen, P. Li, L. Zhang, and Y. Chen, “Stock market trend prediction using high-order information of time series,” IEEE Access, vol. 7, pp. 28299–28308, 2019, doi: 10.1109/ACCESS.2019.2901842.
[9] P. H. Vuong, T. T. Dat, T. K. Mai, P. H. Uyen, and P. T. Bao, “Stock-price forecasting based on XGBoost and LSTM,” Comput. Syst. Sci. Eng., vol. 40, no. 1, pp. 237–246, 2022, doi: 10.32604/CSSE.2022.017685.
[10] M. Durga Prasad, Padmavathi Pragada, P.Pallavi, M. Triveni, and M. Sai Praneeth, “Graphical Interface For Market Asset Pricing Estimation With LSTM,” J. Pharm. Negat. Results, vol. 13, no. 5, pp. 1348–1364, 2022, doi: 10.47750/pnr.2022.13.s05.212.
[11] H. OUKHOUYA, H. KADIRI, K. EL HIMDI, and R. GUERBAZ, “Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models,” Stat. Optim. Inf. Comput., vol. 12, no. 1, pp. 200–209, 2023, doi: 10.19139/soic-2310-5070-1822.
[12] V. Veronica, H. Silaban, S. P. Nasution, and E. Indra, “Analysis Indonesia’s Export Value Forecasting to G20 Countries Using Long Short-Term Memory Neural Network Method,” Sinkron, vol. 7, no. 3, pp. 2008–2017, 2023, doi: 10.33395/sinkron.v7i3.12794.
[13] R. Zazo, A. Lozano-Diez, J. Gonzalez-Dominguez, D. T. Toledano, and J. Gonzalez-Rodriguez, “Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks,” PLoS One, vol. 11, no. 1, 2016, doi: 10.1371/journal.pone.0146917.
[14] W. Wiratama, L. Aulia Alifah, A. Gurusinga, E. Indra, J. Sistem Informasi, and F. Sains Dan Teknologi, “Prediksi Turis Mancanegara ke Indonesia Menggunakan Metode EDA Time Series dan LSTM,” J. Ris. Sist. Inf. Dan Tek. Inform., vol. 8, no. 2, pp. 524–537, 2023, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
[15] T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, 2018, doi: 10.1016/j.ejor.2017.11.054.
[16] S. Palaniappan and N. Palanisamy, “A Profit Prediction model with Time Series Analysis for Retail Store,” Int. J. Pure Appl. Math., vol. 119, no. 12, pp. 1931–1940, 2018, [Online]. Available: http://www.ijpam.eu
[17] M. Afrizal, S. Siswanto, and A. Sudarsono, “Implementation of Forecasting Sales of Electronic Goods Using the Semi Average Method at Alex Electronics Store,” J. Media Comput. Sci., vol. 2, no. 2, pp. 319–330, 2023, doi: 10.37676/jmcs.v2i2.4433.
[18] V. Alapján-, “Exploratory Data Analysis and Forecasting of Covid-19 Vaccination using ARIMA and LSTM Dr.,” vol. 5119, no. 11, pp. 1–23, 2016.
[19] V. G. S. Et al., “Exploratory Data Analysis and Offence Prediction,” Turkish J. Comput. Math. Educ., vol. 12, no. 6, pp. 2761–2768, 2021, doi: 10.17762/turcomat.v12i6.5783.
[20] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, no. March, pp. 1–43, 2020, doi: 10.1016/j.physd.2019.132306.
[21] X. H. Le, H. V. Ho, G. Lee, and S. Jung, “Application of Long Short-Term Memory (LSTM) neural network for flood forecasting,” Water (Switzerland), vol. 11, no. 7, 2019, doi: 10.3390/w11071387.
[22] S. Sim, D. Kim, and H. Bae, “Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 12, pp. 14266–14283, 2023, doi: 10.1109/TPAMI.2023.3319557.
[23] X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” Adv. Neural Inf. Process. Syst., vol. 2015-Janua, pp. 802–810, 2015.
[24] L. Zhang, T. Ji, S. Yu, and G. Liu, “Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method,” Batteries, vol. 9, no. 3, 2023, doi: 10.3390/batteries9030177.
[25] D. M. Gunarto, S. Sa’adah, and D. Q. Utama, “Predicting Cryptocurrency Price Using RNN and LSTM Method,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 1, pp. 1–8, 2023, doi: 10.32736/sisfokom.v12i1.1554.
[26] R. Chakraborty, “Literature study of microwave-assisted digestion using electrothermal atomic absorption spectrometry,” Fresenius. J. Anal. Chem., vol. 355, no. 2, pp. 99–111, 1996, doi: 10.1007/s0021663550099.
[27] M. Sari and A. Asmendri, “Penelitian Kepustakaan (Library Research) dalam Penelitian Pendidikan IPA,” Nat. Sci., vol. 6, no. 1, pp. 41–53, 2020, doi: 10.15548/nsc.v6i1.1555.
[28] G. S. Blair et al., “Data Science of the Natural Environment: A Research Roadmap,” Front. Environ. Sci., vol. 7, no. August, pp. 1–14, 2019, doi: 10.3389/fenvs.2019.00121.
[29] A. Papakonstantinou, M. Batsaris, S. Spondylidis, and K. Topouzelis, “A citizen science unmanned aerial system data acquisition protocol and deep learning techniques for the automatic detection and mapping of marine litter concentrations in the coastal zone,” Drones, vol. 5, no. 1, pp. 1–21, 2021, doi: 10.3390/drones5010006.
[30] B. Zhu, G. Hu, Y. Xie, L. Xu, X. Fan, and Q. Li, “Bayesian Nonparametric Clustering with Feature Selection for Spatially Resolved Transcriptomics Data,” 2023, [Online]. Available: http://arxiv.org/abs/2312.08324
[31] V. Da Poian et al., “Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry,” Front. Astron. Sp. Sci., vol. 10, no. May, pp. 1–17, 2023, doi: 10.3389/fspas.2023.1134141.
[32] S. R. Siregar and R. Widyasari, “Peramalan Harga Crude Oil Menggunakan Metode Long Short-Term Memory (Lstm) Dalam Recurrent Neural Network (Rnn),” J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 4, no. 3, pp. 1478–1489, 2023, doi: 10.46306/lb.v4i3.421.
Published
2024-12-31
How to Cite
GUNAWAN, Ricky et al.
ANALISIS PREDIKSI PENJUALAN TOKO FURNITUR DENGAN METODE LONG SHORT-TERM MEMORY (LSTM).
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 716-725, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1511>. Date accessed: 26 apr. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1511.
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