Indonesian Journal of Electrical Engineering and Computer Science Vol. 18, No. 1, April 2020, pp. 24~31 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v18.i1.pp24-31  24 Journal homepage: http://ijeecs.iaescore.com Forecasting rupiah exchange rate with learning vector quantization neural network Linawati, Made Sudarma, I Putu Oka Wisnawa Department of Electrical and Computer Engineering, Faculty of Engineering, Udayana University, Indonesia Article Info ABSTRACT Article history: Received Jul 23, 2019 Revised Sep 26, 2019 Accepted Oct 8, 2019 The classification technique and data forecasting will probably be one of the techniques that will often be needed in handling or managing big data. So, from that the author analyzes the possible development of the existing algorithms. The purpose is to find possibilities in the use of reliable algorithms in a particular field, then can be adopted and implemented to develop forecasting techniques in the future. Based on these considerations, the authors conducted experiments by applying LVQNN to conduct shortterm forecasting on daily period of the Rupiah exchange rate. The literature that is used as a reference is the discovery of architectural data classification processes that resemble forecasting techniques. So, when there is a combination of Rupiah exchange histories, it is possible to find these combinations into certain classes based on predetermined parameters and historical data combination data and forecast values in the past. In this research the factors chosen as indicators that affect the Rupiah exchange rate are the amount of exports, the amount of imports, the inflation rate and also the world oil price. In this research the highest accuracy value in the testing process for the population reached 99.0991%. The increase in the percentage value of forecasting accuracy is influenced by the composition of the data. In this study the formation of data composition is influenced by distinct data. The selection of parameters which become distinct claused determines how the composition of the data will be formed. If the composition of the data is not correct, the test results will not be good. If the number of weights vector is smaller than the input data, the forecasting accuracy will decrease. Because the weight vector cannot represent data combinations that used during training or testing. Keywords: Artificial neural network Exchange rate Forcasting Learning vector quantization Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Linawati, Department of Electrical and Computer Engineering, Faculty of Engineering, Udayana University, Indonesia. Email: linawati@unud.ac.id 1. INTRODUCTION Currency exchange rate plays an important role in financial markets. Exchange rates are determined in the foreign exchange market. Exchange rate stability is one important consideration for investors to take investment policies. If the exchange rate has high fluctuations, this can affect interest rates, commodity prices and a country's economic policy [1-4]. Then, if it is continued for a long time, it will affect investment policy. Thus, some application of forecasting techniques can help to read the direction of the movement of currency exchange rates, so that it can help investors in reading investment opportunities based on a country's exchange rate factor. Therefore, the risk of failure can be minimized and profit opportunities can be optimized.