Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices

Abstract

Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.