Feed Forward Neural Network (FFNN) model is the best model to forecast the time series data. In this research, The Fuzzy Feed Forward Neural Network (FFFNN) with backward propagation method is used to predict the Jakarta Islamic Index (JII) data time series in 2018. Fuzzy is used as input to the FFNN model because it is overcome the weaknesses of the inaccurate results of the FFNN when the data is unclear or incomplete. The purposes of this research are to explain the procedure to generate the FFFNN model. The steps of the FFFNN model prepare the input data to become a fuzzy number using Growth-S Curve fuzzification; the second is divide the data into two training and testing data, the third is determining the best neural network architecture with different neurons and hidden layers to get the best weights that used for the forecasting model. In this research, the best FFFNN model is built by 19 neurons and one hidden layer with 90% and 10% training and testing data, respectively. Therefore with the model obtained, forecasting produces the value of MSE 0.0018 in training and 0.0004 in testing. From the MSE values obtained, it can be concluded that the forecasting using FFFNN model is reasonable to predict.
Peritika MZ, Sugiyarto, Sunarto. 2012. Diversity of soil macrofauna on different pattern of sloping land agroforestry in Wonogiri,. The purposes of this study were to determine the diversity level of soil macrofauna on different patterns of sloping land agroforestry, in Wonogiri District, Central Java, and to find out the relationship between environmental factors and the level of soil macrofauna diversity. The study was conducted by sampling at three different patterns of agroforestry, namely: pattern of mixed agroforestry (PAC), pattern of teak agroforestry (PAJ), and the pattern of sengon agroforestry (PAS). The field sampling used two methods, namely pit fall traps to obtain above ground macrofauna, and hand sorting methods to obtain underground macrofauna, on land slope of 39%, 35%, and 27%. The data were collected to determine the diversity index of soil macrofauna; and the environmental factors were also measured. The relationship between environmental factors and the diversity index of soil macrofauna was presented in Pearson's correlation analysis. The results showed that the pattern of sloping land agroforestry in Wonogiri District, Central Java had different diversity index of soil macrofauna. The average diversity index of surface macrofauna was the PAC (0.710), PAS (0661), and PAJ (0.417). The average diversity index of underground macrofauna was the PAC (0.887), PAS (0.860), and PAJ (0.843). The diversity index of soil macrofauna in various patterns of sloping land agroforestry showed that there was a correlation with environmental factors.
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