Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE

Abstract

Abstract Forecasting is apply because of complexity and uncertainty faced by high-dimensional data available in the fields of bioinformatics, chemometrics, banking and other applications. A process for systematically estimating what is most likely to happen in the future based on past and present data requires an appropriate forecasting model, so that the difference between what happens and the estimated results can be minimized. To get the right method, a measuring technique is needed to detect the accuracy of forecasting value. In this paper we discuss the technique of measuring forecasting accuracy with Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) using the Random K-Nearest Neighbor (RKNN) method. With the two measuring technique for the horizontal modeling above, the smallest MSE and MAPE values are chosen (the smallest error value). From the results of the analysis of the calculation of forecasting accuracy measurement values during training with RKNN, the MAPE accuracy value is 0.728427% and MSE is 0.545751, while the smallest accuracy value is achieved using MSE which is 0.545751.