Klasifikasi Cuaca Menggunakan Algoritma Fuzzy Mamdani dan CART

Abstract

Weather prediction plays a vital role in sectors such as agriculture, transportation, and disaster mitigation. Extreme weather conditions can lead to unpredictable deviations that may cause significant harm to society. This study aims to predict weather conditions using the Fuzzy Mamdani algorithm and CART (Classification and Regression Tree). A total of 1,461 daily historical weather records from Seattle, United States, were obtained from the Kaggle website “Weather Prediction.” The fuzzy system was applied to convert numerical weather parameters—precipitation, maximum temperature, minimum temperature, and wind speed—into representative scores based on expert-defined rules. These fuzzy scores were then used as additional features in training the CART model to enhance weather classification accuracy. The dataset was split into 80% training data (1,168 records) and 20% testing data (293 records). Evaluation results show that the integration of Fuzzy and CART achieved an accuracy of 81.23% on the testing set, with high precision, recall, and F1 score for dominant categories such as sun and rain. This study demonstrates that the combination of fuzzy logic and decision trees is effective for weather classification based on historical data.