Implementation of Supervised Learning Algorithm to Predict Climate Factors in Pangkalpinang

Abstract Currently, the world is experiencing erratic climate conditions, characterized by an increase in frequency and intensity of extreme weather, as well as significant climate changes such as an increase in global temperatures. Pangkalpinang city is one of cities with the highest population density in Bangka Belitung Islands. This population density contributes to climate change through increased greenhouse gas emissions from transportation activities, industry, and high energy consumption. Conversely, climate change also has an impact on environmental conditions and the quality of life, making it important to predict climate factors to effectively manage their impact. The purpose of this research is to determine the prediction of climate factors in Pangkalpinang City. In this research, three Supervised Learning algorithm research methods were used, including Linear Regression, ANN (Artificial Neural Network), and XGBoost Regressor (Extreme Gradient Boosting Regressor). Based on the results of the research that has been conducted, it shows that the XGBoost Regressor model provides the best performance with an MSE value of 79.304 and the smallest MAPE value of 16.912. Beside it, the R-squared value is 0.654. This model shows a good ability to predict climate variables compared to other methods. This finding can be the basis for more appropriate policy making in anticipating and managing the impacts of climate change in Pangkalpinang.