<![CDATA[Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.]]>
Copyrights © 2024
