Comprehensive Approach to Weather Prediction with the Random Forest Algorithm

Abstract

Weather is an air condition that is very important in everyday life. Accurate weather predictions can help people anticipate and deal with weather changes that can have an impact on daily activities. This research aims to develop an effective weather prediction model using machine learning algorithms. In this research, we use three popular machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), and Decision Tree. The data used consists of historical weather data, including air temperature, air humidity, rainfall, wind direction, air pressure, wind speed, and solar radiation. The research results show that the Random Forest algorithm has the highest accuracy, with a prediction rate of 83%. The SVM algorithm is next, with a prediction rate of 78%, while the Decision Tree algorithm has a prediction rate of 72%. These findings show that Random Forest is the most effective algorithm in predicting weather, especially in predicting air temperature and rainfall. This research has significant practical implications in increasing the accuracy of weather predictions, which can help society anticipate and deal with weather changes that can impact in daily activities. In the future, this research can be used as a basis for developing more accurate and reliable weather prediction systems.