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.
Forecasting is an activity to predict what will happen in the future by paying attention to information from the past and the present. A regression model that explains the past movement of the variable itself and also all other variables without distinguishing which endogenous and exogenous variables are called Vector Autoregressive (VAR). But in practice, endogenous variables are supported by exogenous variables. The Vector Autoregressive Exogenous (VARX) model is a development of the VAR with the addition of exogenous variables. The purpose of this study is to form the best model in the VAR method with the addition of an exogenous variable in the form of an effect calendar for forecasting the number of tourists coming to the Special Region of Yogyakarta (DIY). The data used in this study are time series data for 10 years from January 2009 to December 2018 in the form of tourist visit data in the Special Region of Yogyakarta (DIY). The results obtained indicate that the effect calendar variable that affects tourist visitor data in DIY is at Christmas. After being analyzed using MAPE, the best model is the VARX (1.0) model which produces a smaller. So, it can be concluded that the VARX model with the addition of an effects calendar is suitable for predicting tourist visits.This is an open access article under the CC-BY-SA license.
Chicken Swarm Optimization (CSO) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behaviour of chicken swarm. Data clustering is used in many disciplines and applications. It is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, CSO is used for data clustering. The performance of the proposed CSO was assessed on several data sets and compared with well known and recent metaheuristic algorithm for clustering: Particle Swarm Optimization (PSO) algorithm , Cuckoo Search (CS) and Bee Colony Algorithm (BC). The simulation results indicate that CSO algorithm have much potential and can efficiently be used for data clustering.
Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.
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