Peramalan Jumlah Incident Information Technology PT XYZ Menggunakan Artificial Neural Network (ANN)

Abstract

Forecasting is a technique for predicting events that will occur in the future using historical data as a comparison In this research, researchers try to determine the performance of the ANN method for forecasting the number of incidents at PT XYZ and build an application for forecasting the number of incidents at PT XYZ. This research uses the Mean Absolute Percentage Error (MAPE) evaluation metric as the evaluation metric that will be interpreted. The smaller the MAPE value, the better the model architecture. The best model is the model that produces the smallest MAPE value and does not experience underfitting or overfitting conditions. Based on the research results, it was found that all the best models from each model architecture produced a MAPE value of less than 10 and did not experience underfitting or overfitting conditions. Therefore, it can be interpreted that all the models produced are very accurate to be used as incident forecasting models at PT XYZ for the next 4 weeks. The website-based incident forecasting application created to predict the number of incidents for the next 4 weeks using the best model that has been previously saved also produces a MAPE value of less than 10 and does not experience underfitting and overfitting conditions.