Implementation of Moving Average and Soft Computing algorithm to support planting season calendar forecasting system on mobile device

Abstract

Moving Average and Soft Computing is an algorithm widely used for forecasting especially for rainfall forecasting. Rainfall forecasting information is crucial for agriculture in Bandung Regency so that a planting season calendar suitable for each plant could be given. To get a better result, Five types of Moving Average algorithm were used on data preprocessing. Rainfall forecasting was done by using four hybrid algorithm on Soft Computing, which were ANFIS, Evolving Fuzzy, Fuzzy-Grammatical Evolution, and ANN-Nested Genetic Algorithm. Chromosome representation done on ANN-Nested Genetic Algorithm is different than common Evolving Neural Network algorithm. Result of rainfall forecasting was used to forecast crops, corns, and potatoes planting calendar. The experiment shown that ANFIS algorithm gives best MAPE training of 0.1065 but ANN-NGA algorithm gives best MAPE testing of 0.127. Planting calendar forecasting used the best prediction model of Modified Weighted Moving Average and ANFIS with accuracy of 91.67% for crops, corns, and potatoes.