The Prediksi Curah Hujan Pada Stasiun BMKG (CITEKO) Menggunakan Metode Backpropogation Neural Network

Accurate rainfall prediction is needed to improve the performance of land that always uses rainfall data. Data mining or often called knowledge discovery in databases (KDD) is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data. In predicting rainfall, there are several conditions that can be observed as reference data to predict rainfall, namely wind speed, temperature, and air humidity. In this research, a backpropagation artificial neural network prediction method is developed that can be used in predicting future rainfall. The backpropogation artificial neural network method that was built produced an accuracy value of 95.36%, a precision value of 90.50%, a recall value of 97.50% and an f-measure value of 92.00%