Prediksi Cuaca Menggunakan Data Historis dengan Algoritma Regresi Linear untuk Analisis Perubahan Suhu
-
Egi Pratama
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Muhammad Fatchan Universitas Pelita Bangsa, Bekasi, Indonesia
- Ahmad Aguswin Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
Tokyo, the capital of Japan located on the state of Honshu, is facing with subtropical climate complexity, combining extreme temperature variations reached in hot summer season (>35 degrees) and cold winter season temperatures below 0 degrees. Current research explored the regression linear algorithm potential to predict daily maximum temperature within the context of complex urban weather dynamics. Based on the meteorology dataset collected in total of 639 days including key variables of temperature, humidity, rainfall, and air pressure, study developed weather prediction model. The outcomes demonstrated exceptional performance with Root Mean Squared Error at 0.80 and R-squared of 0.99, showing the near full coverage of model’s ability to capture all possible weather variability patterns. As a result, the research findings not only confirmed the effectiveness of linear regression for urban weather prediction but also open the possibility of similar model integration within more sophisticated weather forecast systems. Data-centered approach made significant contribution to the modern weather prediction technology responsive to urban society requirement.
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