Prediction of Cropping Season Onset Using Machine Learning as a Project-Based Learning (PjBL) Module in a Computational Physics Course
DOI:
https://doi.org/10.22437/proca.v2i3.59986Keywords:
cropping season onset, climate change, machine learning, computational physics, project-based learningAbstract
Climate change has led to increasing air temperatures and changing rainfall patterns, which directly affect the agricultural sector, particularly in determining cropping calendars. Conventional cropping calendars have become less accurate, potentially reducing crop productivity and increasing the risk of crop failure. The challenge of predicting cropping calendars is closely related to computer programming concepts taught in the Computational Physics course, and integrating these two areas is strategically important to support Indonesia's national food security program, particularly in Jambi Province. Objective: This study aims to (1) develop a climate change-based cropping onset calendar using a machine learning approach, and (2) integrate the development of this cropping calendar as a project in the Computational Physics course. Methods: The study used rainfall, air temperature, and large-scale climate indices, including the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden–Julian Oscillation (MJO) as predictors. The research methodology consists of data preprocessing, exploratory data analysis, and predictive modeling using a machine learning algorithm (Random Forest). Model performance is evaluated using Mean Absolute Error (MAE). Results: The model predicted MT1 with the MAE improving from 1.0 to 0.67 dasarian after feature selection. However, MT2 prediction remained less accurate, with the MAE decreasing only slightly from 3.3 to 3.0 dasarians. The modeling process can be integrated into the Computational Physics course through a project-based learning approach. Students participate in data processing, model development, and result interpretation, thereby enhancing their computational and data analysis skills through real-world problem-solving. Conclusion: The outputs of this research include an adaptive planting time prediction model that accounts for climate change, a data-driven cropping calendar prototype, and a contextual learning innovation for Computational Physics education. This study is expected to contribute both to the advancement of data-driven agricultural science and to the improvement of teaching quality in higher education
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