Machine Learning-Based Climate Prediction in Indonesia: A Baseline Experiment
Faisal Rahutomo, Bambang Harjito · International Journal of Advanced Computer Science and Applications · 2025
This study presents the results of a series of machine learning experiments conducted on Indonesian climate data collected between 2010 and 2020. The findings offer a comparative foundation for future research. Weather prediction remains a significant challenge due to the complex interplay of various climatic factors. Weather stations typically record data at hourly or daily intervals, resulting in large volumes of historical weather information. When appropriately processed, this extensive dataset offers valuable opportunities for predictive modeling. The study explores two primary approaches to leveraging big data for weather forecasting. The first employs a machine learning classification technique to predict categorical weather conditions based on existing feature values. The second utilizes time series forecasting to predict continuous weather parameters using historical data. Multiple classification and forecasting algorithms were evaluated and compared. Notably, the year-on-year forecasting approach outperformed several modern techniques, including deep learning, in terms of predictive accuracy. Despite the application of deep learning, classification models achieved a maximum accuracy of only 0.811. Forecasting methods generally produced a mean absolute percentage error (MAPE) of 3–4%. However, year-on-year forecasting—identified through exploratory data visualization—reduced the prediction error to below 1.6%. Another key contribution of this research is the emphasis on the critical role of data visualization prior to algorithmic modeling. The findings highlight the importance of human intervention in the early stages of data analysis, particularly for visual exploration and feature assessment. Classification models were found to underperform due to overly generalized feature representations. In contrast, forecasting techniques, supported by informed human-guided preprocessing, yielded more reliable and accurate results.