Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors

Electronic journal (e-journal) management comprises several aspects, specifically pageviews, sessions, visitors, and new visitors. Sessions or the number of unique visitors from a journal page is an essential indicator of a journal's outcome. Therefore, it is necessary to forecast the number of unique visitors to determine the strategy for developing a journal. Thus, it is expected to be able to accelerate the journal accreditation system in the future. In this study, this paper predicts the number of unique visitors to the journal by developing a time series forecasting model. Forecasting was done by applying the Backpropagation. The method has the advantage of being able to adapt to changes that occur in the input and output values. There are three time series data input models for this research, specifically three days, seven days and 14 days. The accuracy of forecasting results was measured using the MAPE evaluation of several forecasting models and BPNN architecture. The results show that the best forecasting is using forecasting model 1 and architecture 2-5-1 with an accuracy value of 69.9%. Thus, the performance of the Neural Network in this study is relatively good.