Rainfall prediction in Kemayoran Jakarta using Hybrid Genetic Algorithm (GA) and Partially Connected Feedforward Neural Network (PCFNN)

The weather changes easily these days, that is difficult to predict. Yet, weather forecasting is the important and useful thing in all aspects of life for instance, in the agriculture field to decide the time of planting. Thus, weather forecast of rain fall intensity particularly in region of Kemayoran Jakarta is conducted in this research. The forecast system built uses Hybrid Genetic Algorithm (GA) and Partially Connected Feedforward Neural Network (PCFNN). In this research, optimum weight and connection neural is optimized with the type of PCFNN. Previously, it has been conducted a research using Evolving Neural Network with all connected neural network and the result was MAPE prediction result rainfall 38.82% or accuracy as 61.18%. Yet, based on the experiment hybrid algorithm between GA and PCFNN, it is gained the MAPE value 35.20% or accuracy as 64.80% with the solution of data missing value in rainfall intensity replaced by 0 (zero) and MAPE 18.48% or accuracy 81.52% for missing value solution in rainfall intensity replaced by mean value.