Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory
DOI:
https://doi.org/10.15575/join.v9i2.1113Keywords:
Garlic, LSTM, Prediction ModelsAbstract
References
[1] “Outlook bawang putih Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian 2020,” 2020.
[2] Dirjen Hortikultura. 2017. Pengembangan bawang putih nasional. Kementerian Pertanian.
[3] Qian, P., Zhang, Q., Peng, L., Zhu, Q., Wang, X., & Chen, X. A novel 2Atts-LSTM model with spatio-temporal attention for short-term wind speed forecasting. Renewable Energy, 173, 931-945.2021. https://doi.org/10.1016/j.renene.2021.04.064.
[4] Nurkholis A, Sitanggang IS, Annisa, Sobir. Spatial Decision Tree Model For Garlic Land Suitability Evaluation. IAES International Journal of Artificial Intelligence (IJ-AI). 2021.
[5] Purwayoga V. Analisis Pemetaan Kesesuaian Lahan Untuk Bawang Putih dengan Pendekatan Spatial Clustering [tesis] Bogor: Institut Pertanian Bogor. 2019.
[6] L. Ni et al., “Streamflow and rainfall forecasting by two long short-term memory-based models,” J Hydrol (Amst), vol. 583, p. 124296, Apr. 2020, doi: 10.1016/j.jhydrol.2019.124296.
[6] I. Renewable Energy Agency, RENEWABLE POWER GENERATION COSTS IN 2018. 2019. [Online]. Available: www.irena.org
[7] Gartner L Barbara, “Plant Stems: Physiology and Functional Morphology,” 1995.
[8] P. Liu et al., “Distinct Quality Changes of Garlic Bulb during Growth by Metabolomics Analysis,” J Agric Food Chem, vol. 68, no. 20, pp. 5752–5762, May 2020, doi: 10.1021/acs.jafc.0c01120.
[9] W. Chen, L. Yang, B. Zha, M. Zhang, and Y. Chen, “Deep learning reservoir porosity prediction based on multilayer long shortterm memory network,” GEOPHYSICS, vol. 85, no. 4, pp. WA213–WA225, Jul. 2020, doi: 10.1190/geo2019-0261.1
[10] S. Mahjoub, L. Chrifi-Alaoui, B. Marhic, and L. Delahoche, “Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks,” Sensors, vol. 22, no. 11, Jun. 2022, doi: 10.3390/s22114062.
[11] L. Liu et al., “Transient rotor angle stability prediction method based on SVM and LSTM network,” Dianli Zidonghua Shebei/Electric Power Automation Equipment, vol. 40, no. 2, 2020, doi: 10.16081/j.epae.202001009.
[12] G. S. Vidya and V. S. Hari, “LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic,” J Signal Process Syst, vol. 95, no. 2–3, pp. 161–176, Mar. 2023, doi: 10.1007/s11265-022-01831-x.
[13] X. Liu and Z. Lin, “Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory,” Energy, vol. 227, Jul. 2021, doi: 10.1016/j.energy.2021.120455.
[14] M. Marani, M. Zeinali, V. Songmene, and C. K. Mechefske, “Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling,” Measurement, vol. 177, p. 109329, Jun. 2021, doi: 10.1016/j.measurement.2021.109329.
[15] M. Dehghan Manshadi, M. Ghassemi, S. M. Mousavi, A. H. Mosavi, and L. Kovacs, “Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory,” Energies (Basel), vol. 14, no. 16, p. 4867, Aug. 2021, doi: 10.3390/en14164867.
[16] J. Zhao, F. Deng, Y. Cai, and J. Chen, “Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction,” Chemosphere, vol. 220, pp. 486–492, Apr. 2019, doi: 10.1016/j.chemosphere.2018.12.128.
[17] P. Gong, Y. Ma, C. Li, X. Ma, and S. H. Noh, “Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks,” Apr. 2023, [Online]. Available: http://arxiv.org/abs/2304.08925.
[18] J. Qian, M. Zhu, Y. Zhao, and X. He, “Short-term wind speed prediction with a two-layer attention-based lstm,” Computer Systems Science and Engineering, vol. 39, no. 2, pp. 197–209, 2021, doi: 10.32604/csse.2021.016911.
[19] F. R. Alharbi and D. Csala, “Wind speed and solar irradiance prediction using a bidirectional long short-term memory model based on neural networks,” Energies (Basel), vol. 14, no. 20, Oct. 2021, doi: 10.3390/en14206501.
[20] M. Kamal Wisyaldin, G. Maya Luciana, H. Pariaman, and P. Pembangkitan Jawa Bali, “Pendekatan Long Short-Term Memory untuk Memprediksi Kondisi Motor 10 kV pada PLTU Batubara,” vol. 9, no. 2, 2020, doi: 10.33322/kilat.v9i2.997.
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2024 Muhammad Safrul Safrudin, Imas Sukaesih Sitanggang, Hari Agung Adrianto, Syarifah Aini

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
-
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License