Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Abstract

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.