Comparison of Random Forest, K-Nearest Neighbors, Decision Tree, and Neural Network for Predicting Rainfall - Fariyani, Fariyani; Sunarno; Iqbal; Upik Nurbaiti; Ian Yulianti (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
Erratic rainfall due to climate change has significant impacts on the environment, agriculture and economy. To mitigate these impacts, a reliable rainfall prediction model is needed. Erratic rainfall due to climate change affects various sectors of life, so a reliable prediction model is needed to support data-based decision making. This study aims to compare the performance of Random Forest, k-Nearest Neighbors (kNN), Decision Tree, and Neural Network algorithms in predicting rainfall using observation data from the Citeko Meteorological Station. The data used include weather parameters such as temperature, humidity, and air pressure. The analysis was carried out using Orange software to evaluate the accuracy, precision, and computation time of each model. The results showed that Random Forest had the highest accuracy, while Neural Network showed consistent performance on more complex datasets. The kNN algorithm gave good results with the optimal number of neighbors, but was less efficient on large datasets. Decision Tree was easier to interpret but was prone to overfitting. This study provides insight into the most appropriate algorithm for rainfall prediction based on the characteristics of the data available.