The extension of Moore-Penrose generalized inverse for extreme learning machine in forecasting USD/IDR exchange rate as impact of COVID-19

Abstract

Since the beginning of the COVID-19 pandemic, the Indonesian economy has undergone changes which have caused the Rupiah exchange rate against the US Dollar to increase and weaken. Throughout March to September 2020, the Rupiah exchange rate depreciated by 2.75% - 4.57% with a range of Rp. 14,000 – Rp. 16,575 per US Dollar. Depreciation has a negative impact on the Indonesian economy because it makes product prices relatively cheaper for other countries. Therefore, it is necessary to do a forecast to determine the exchange rate of the Rupiah against the US Dollar in the future. Forecasting is an activity carried out using existing data to predict something in the future. In forecasting USD/IDR Exchange Rate using the Extreme Learning Machine method. This method does not require parametric assumptions and has a faster learning speed feedforward by determining the weights and biases on the network randomly. The ELM formulation leads to solving a system of linear equations in terms of unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations is obtained using the Moore-Penrose Pseudo Inverse. The results of data analysis resulted in the optimum ELM network architecture (17-49-1), namely 17 neurons in the input layer, 49 neurons in the hidden layer, and 1 neuron in the output layer. The network is obtained from MSE which produces the smallest error value, which is 0.000338 on training data and 0.000139 on testing data. With this network, the results of forecasting USD/IDR Exchange Rate with MAPE are 0.2383951%. Forecasting results show that the Rupiah exchange rate has appreciation and is quite stable as expected by the government in strengthening the Indonesian economy during the COVID-19 pandemic.