Authors:
Doni El Rezen Purba
1
;
Herman Mawengkang
2
and
Tulus
2
Affiliations:
1
Faculty of Computer Sciences and Information Technology, Universitas Sumatera Utara, Medan - Indonesia, Indonesia
;
2
Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Medan - Indonesia
Keyword(s):
Data Mining, Forecasting, Optimization, Training Function.
Abstract:
Forecasting or predicting future events is important to take into account in order for an activity to proceed properly. Flights predict the weather forecast, the banking industry predicts the price of currency, the health world predicts the disease, the retail business predicts total sales. prediction or forecasting of events is calculated using past data, usually in the form of time series. Artificial neural networks are capable of forecasting time-series data. Forecasting results with artificial neural network is influenced from the network architecture model is determined, one of which determination of training function. Based on research conducted by Aggarwal KK (et al 2005) and Murru & Rossini, R. (2016), using Bayesian regularization training function in their research, this research uses the algorithm for time clock data forecasting process with several model of layer count and number of neurons. The results obtained with the number of 3 layers and each neuron of 36, 12, 6 for
the best process performance, and the number of neurons 24, 12, 6 for the shortest iteration process.
(More)