Main Article Content

Gunawan Gunawan
Wresti Andriani
Aminnur Aimar Akbar


This study explores the Application of Machine Learning for Short-Term Climate Prediction in Indonesia, focusing on enhancing forecast accuracy through advanced computational models. The primary objective was to develop and validate Random Forest and Support Vector Machine (SVM) models to predict short-term climate conditions accurately across ten major Indonesian cities. Employing a quantitative approach, the study utilized experimental design, rigorous data analysis, and model validation using historical weather data from April 2024 provided by the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The results indicate that both Random Forest and SVM significantly outperform traditional climate prediction models, with Random Forest achieving an average accuracy of 87.5% and SVM 85.2%. These findings underscore the potential of machine learning to revolutionize short-term climate predictions in regions with complex meteorological dynamics like Indonesia, offering substantial implications for disaster preparedness, agricultural planning, and urban management. Future research can expand upon these models by incorporating real-time data and exploring deep learning techniques to enhance predictive reliability further


Download data is not yet available.

Article Details

How to Cite
Gunawan, G., Andriani, . W. . and Aimar Akbar, A. . (2024) “Application of machine learning for short-term climate prediction in Indonesia”, Jurnal Mantik, 8(1), pp. 828-837. doi: 10.35335/mantik.v8i1.5215.
Ali, U., Shamsi, M. H., Hoare, C., Mangina, E., & O’Donnell, J. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246, 111073.
Bienvenido-Huertas, D., de la Hoz-Torres, M. L., Aguilar, A. J., Tejedor, B., & Sánchez-García, D. (2023). Holistic overview of natural ventilation and mixed mode in built environment of warm climate zones and hot seasons. Building and Environment, 110942.
BMKG. (2024). climate data in Indonesia.
Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z. (2023). Machine learning methods in weather and climate applications: A survey. Applied Sciences, 13(21), 12019.
Das, R., Middya, A. I., & Roy, S. (2022). High granular and short term time series forecasting of pm 2.5 air pollutant-a comparative review. Artificial Intelligence Review, 55(2), 1253–1287.
Esmaeili-Gisavandani, H., Lotfirad, M., Sofla, M. S. D., & Ashrafzadeh, A. (2021). Improving the performance of rainfall-runoff models using the gene expression programming approach. Journal of Water and Climate Change, 12(7), 3308–3329.
Gonzalez-Trevizo, M. E., Martinez-Torres, K. E., Armendariz-Lopez, J. F., Santamouris, M., Bojorquez-Morales, G., & Luna-Leon, A. (2021). Research trends on environmental, energy and vulnerability impacts of Urban Heat Islands: An overview. Energy and Buildings, 246, 111051.
Jaseena, K. U., & Kovoor, B. C. (2022). Deterministic weather forecasting models based on intelligent predictors: A survey. Journal of King Saud University-Computer and Information Sciences, 34(6), 3393–3412.
Jose, D. M., Vincent, A. M., & Dwarakish, G. S. (2022). Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Scientific Reports, 12(1), 4678.
Kendon, E. J., Prein, A. F., Senior, C. A., & Stirling, A. (2021). Challenges and outlook for convection-permitting climate modelling. Philosophical Transactions of the Royal Society A, 379(2195), 20190547.
Klemm, C., & Vennemann, P. (2021). Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches. Renewable and Sustainable Energy Reviews, 135, 110206.
Latif, S. D., Hazrin, N. A. B., Koo, C. H., Ng, J. L., Chaplot, B., Huang, Y. F., El-Shafie, A., & Ahmed, A. N. (2023). Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches. Alexandria Engineering Journal, 82, 16–25.
Lin, S., Zheng, H., Han, B., Li, Y., Han, C., & Li, W. (2022). Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica, 17(4), 1477–1502.
Merz, B., Kuhlicke, C., Kunz, M., Pittore, M., Babeyko, A., Bresch, D. N., Domeisen, D. I. V, Feser, F., Koszalka, I., & Kreibich, H. (2020). Impact forecasting to support emergency management of natural hazards. Reviews of Geophysics, 58(4), e2020RG000704.
Ngila, P. M., Chiawo, D. O., Owuor, M. A., Wasonga, V. O., & Mugo, J. W. (2023). Mapping suitable habitats for globally endangered raptors in Kenya: Integrating climate factors and conservation planning. Ecology and Evolution, 13(9), e10443.
Nurwanda, A., & Honjo, T. (2020). The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustainable Cities and Society, 52, 101772.
Polkinghorne, M., Pearson, N., van Duivenvoorde, W., Nayati, W., Tahir, Z., Ridwan, N. N. H., Forrest, C., Tan, N. H., Popelka-Filcoff, R., & Morton, C. (2024). Reuniting orphaned cargoes: Recovering cultural knowledge from salvaged and dispersed underwater cultural heritage in Southeast Asia. Marine Policy, 163, 106074.
Shetty, S. H., Shetty, S., Singh, C., & Rao, A. (2022). Supervised machine learning: algorithms and applications. Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, 1–16.
Sothe, C., De Almeida, C. M., Schimalski, M. B., La Rosa, L. E. C., Castro, J. D. B., Feitosa, R. Q., Dalponte, M., Lima, C. L., Liesenberg, V., & Miyoshi, G. T. (2020). Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data. GIScience & Remote Sensing, 57(3), 369–394.
Streets, D. G., & Glantz, M. H. (2000). Exploring the concept of climate surprise. Global Environmental Change, 10(2), 97–107.
Susanto, J., Zheng, X., Liu, Y., & Wang, C. (2020). The impacts of climate variables and climate-related extreme events on island country’s tourism: Evidence from Indonesia. Journal of Cleaner Production, 276, 124204.
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Philip, S. Y. (2022). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering, 35(8), 8052–8072.
Wu, H., & Levinson, D. (2021). The ensemble approach to forecasting: A review and synthesis. Transportation Research Part C: Emerging Technologies, 132, 103357.
Zennaro, F., Furlan, E., Simeoni, C., Torresan, S., Aslan, S., Critto, A., & Marcomini, A. (2021). Exploring machine learning potential for climate change risk assessment. Earth-Science Reviews, 220, 103752.
Zhang, X., Mohanty, S. N., Parida, A. K., Pani, S. K., Dong, B., & Cheng, X. (2020). Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access, 8, 30223–30233.
Zhang, Y., Liu, J., & Shen, W. (2022). A review of ensemble learning algorithms used in remote sensing applications. Applied Sciences, 12(17), 8654.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., & Mahecha, M. D. (2020). A typology of compound weather and climate events. Nature Reviews Earth & Environment, 1(7), 333–347.