Improving Multiclass Rainfall Prediction with Multilayer Perceptron and SMOTE: Addressing Class Imbalance Challenges - Cahyani, Nita; Putri, Wardiana Adinda; Irsyada, Rahmat (Yayasan Cita Cendekiawan Al Khwarizmi, 2024)
Rainfall is a key climate element that affects weather patterns and human activities, especially in agriculture and daily life. Therefore, accurately classifying rainfall is crucial for predicting future rainfall amounts. This study uses the Multilayer Perceptron (MLP) classification method, a neural network algorithm, to classify rainfall. The dataset, sourced from the BMKG website, has a class imbalance, requiring using the SMOTE (Synthetic Minority Over-sampling Technique) technique. The research compares the performance of MLP with and without SMOTE. The results show that the best model was achieved with SMOTE. MLP without SMOTE achieved an accuracy of 75%, sensitivity of 40.34%, specificity of 86.15%, and an AUC of 63.25%. In comparison, MLP with SMOTE achieved an accuracy of 71.27%, sensitivity of 71.14%, specificity of 90.30%, and an AUC of 80.72%. Although accuracy decreased, the overall evaluation, particularly the AUC, improved significantly. Therefore, the SMOTE technique effectively addresses the class imbalance issue in rainfall classification.