Application of unsupervised K Nearest Neighbor (UNN) and Learning Vector Quantization (LVQ) methods in predicting Rupiah to Dollar

Abstract

One of the factors in a country's economy is the exchange value of the currency towards another currency. The exchange value of Rupiah towards Dollar (USA) can quickly change depending on the environmental conditions and has a huge impact for the Indonesian Government. In this research, Learning Vector Quantization (LVQ) and Unsupervised K Nearest Network (UNN) was implemented in predicting the currency value towards dollar. The UNN method was used to predict the selling value of the currency, the LVQ method was used to predict the buying value of the currency. The input data that is used is the selling, buying and interest data times series of the currency from the central back of the United States. From the research result and discussions that was made, UNN can achieve the lowest MAPE, which is 1,544% with the amount of data as much as 25 and the LVQ algorithm can accurately achieve a forecast with the amount of data as much as 25 with the learning rate of 0,075. The amount of trained data and the many patterns that exist in one LVQ class method can affect the result of the study and the result of the system.