Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector
Keywords:
Agriculture, Cross-Validation, Grid Search, Rain Prediction, Random Forest
Abstract
Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.
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References
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[10] R. Rofik, R. A. Hakim, J. Unjung, B. Prasetiyo, and M. A. Muslim, “Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, Vol. 23, No. 2, PP. 419–430, 2024, DOI: https://doi.org/10.30812/matrik.v23i2.3566.
[11] S. Rasheed, G. K. Kumar, D. M. Rani, M. V. V. P. Kantipudi, and M. Anila, “Heart Disease Prediction Using GridSearchCV and Random Forest,” EAI Endorsed Transactions on Pervasive Health and Technology, Vol. 10, No. 1, PP. 1–8, 2024, DOI: https://doi.org/10.4108/eetpht.10.5523.
[12] S. George and B. Sumathi, “Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction,” International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 11, No. 9, PP. 173-178, 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110920.
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[15] A. J. Hill, G. R. Herman, and R. S. Schumacher, “Forecasting severe weather with random forests,” Monthly Weather Review, Vol. 148, No. 5, PP. 2135–2161, 2020, DOI: http://doi.org/10.1175/MWR-D-19-0344.1.
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[17] S. Efendi and P. Sihombing, “Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, Vol. 21, No. 3, PP. 583–594, 2022, DOI: http://doi.org/10.30812/matrik.v21i3.1898.
[18] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Frontiers in Energy Research, Vol. 9, No. 29, PP. 1-17, 2021. DOI: https://doi.org/10.3389/fenrg.2021.652801.
[19] Y. Shaikh, V. Parvati, and S. R. Biradar, “Early disease prediction algorithm for hypertension-based diseases using data aware algorithms,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 27, No. 2, PP. 1100–1108, 2022, DOI: http://doi.org/10.11591/ijeecs.v27.i2.pp1100-1108.
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[21] M. Adnan, A. A. S. Alarood, M. I. Uddin, and I. ur Rehman, “Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models,” PeerJ Computer Science, Vol. 8, No. 1 PP. 1–29, 2022, DOI: http://doi.org/10.7717/PEERJ-CS.803.
[22] N. W. Hesty, D. G. Cendrawati, A. Aminuddin, B. Pranoto, and A. Fahim, “Estimasi Potensi Energi Angin Indonesia Menggunakan Model Weather Research and Forecast - Four Dimension Data Assimiliation (WRF-FDDA),” Jurnal Sains Dirgantara, Vol. 19, No. 2, PP. 11–20, 2022, DOI: http://doi.org/10.30536/j.jsd.2022.v19.3614.
Published
2024-06-15
How to Cite
Putra, A., Azmi, M., Wijayanto, H., Utama, S., & Wedashwara Wirawan, I. G. P. (2024). Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(3), 519-528. https://doi.org/https://doi.org/10.30812/matrik.v23i3.3891
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