Konfigurasi Hyperparameter Long Short Term Memory untuk Optimalisasi Prediksi Penjualan
(1) Universitas Krisnadwipayana
(2) Universitas Krisnadwipayana
(3) Universitas Krisnadwipayana
(*) Corresponding Author
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R. M. van Steenbergen and M. R. K. Mes, “Forecasting demand profiles of new products,” Decis. Support Syst., vol. 139, p. 113401, Dec. 2020, doi: 10.1016/j.dss.2020.113401.
L. R. Berry, P. Helman, and M. West, “Probabilistic forecasting of heterogeneous consumer transaction–sales time series,” Int. J. Forecast., vol. 36, no. 2, pp. 552–569, Apr. 2020, doi: 10.1016/j.ijforecast.2019.07.007.
H. Abbasimehr, M. Shabani, and M. Yousefi, “An optimized model using LSTM network for demand forecasting,” Comput. Ind. Eng., vol. 143, p. 106435, May 2020, doi: 10.1016/j.cie.2020.106435.
L. Liang and X. Cai, “Forecasting peer-to-peer platform default rate with LSTM neural network,” Electron. Commer. Res. Appl., vol. 43, p. 100997, Sep. 2020, doi: 10.1016/j.elerap.2020.100997.
K. M. Sabu and T. K. M. Kumar, “Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala,” Procedia Comput. Sci., vol. 171, pp. 699–708, 2020, doi: 10.1016/j.procs.2020.04.076.
S. Muzaffar and A. Afshari, “Short-Term Load Forecasts Using LSTM Networks,” Energy Procedia, vol. 158, pp. 2922–2927, Feb. 2019, doi: 10.1016/j.egypro.2019.01.952.
M. Sarkar and A. De Bruyn, “LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning,” J. Interact. Mark., vol. 53, pp. 80–95, Feb. 2021, doi: 10.1016/j.intmar.2020.07.002.
S. Helmini, N. Jihan, M. Jayasinghe, and S. Perera, “Sales forecasting using multivariate long short term memorynetwork models,” PeerJ Prepr., vol. 7, pp. 1–16, 2019, doi: https://doi.org/10.7287/peerj.preprints.27712v1.
A. Khumaidi, “Data Mining For Predicting The Amount Of Coffee Production Using CRISP-DM Method,” J. Techno Nusa Mandiri, vol. 17, no. 1, pp. 1–8, Feb. 2020, doi: 10.33480/techno.v17i1.1240.
R. Nisbet, G. Miner, and K. Yale, “A Data Preparation Cookbook,” in Handbook of Statistical Analysis and Data Mining Applications, Elsevier, 2018, pp. 727–740.
R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” Procedia Comput. Sci., vol. 173, pp. 130–139, 2020, doi: 10.1016/j.procs.2020.06.017.
W. Li, B. Wang, J. Liu, G. Zhang, and J. Wang, “IGBT aging monitoring and remaining lifetime prediction based on long short-term memory (LSTM) networks,” Microelectron. Reliab., vol. 114, p. 113902, Nov. 2020, doi: 10.1016/j.microrel.2020.113902.
C. Panem, V. R. Gad, and R. S. Gad, “Sensor’s data transmission with BPSK using LDPC (Min-Sum) error corrections over MIMO channel: Analysis over RMSE and BER,” Mater. Today Proc., vol. 27, pp. 571–575, 2020, doi: 10.1016/j.matpr.2019.12.039.
S. Lightstone, T. Teorey, and T. Nadeau, “Denormalization,” in Physical Database Design, Elsevier, 2007, pp. 337–355.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, p. 105524, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.
DOI: http://dx.doi.org/10.30998/faktorexacta.v15i4.15286
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.