Classification of the IDR-USD Exchange Rate with Multilayer Perceptron Based on Detection Rate

Autor:	Fatma Sari Hutagalung, Fanny Ramadhani, Al-Khowarizmi Al-Khowarizmi
Rok vydání:	2021
Předmět:	Artificial neural network Validation test Computer science business.industry Supervised learning Machine learning computer.software_genre Field (computer science) Set (abstract data type) Exchange rate Multilayer perceptron Artificial intelligence Detection rate business computer
Zdroj:	JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING. 5:76-83
ISSN:	2549-6255 2549-6247
Popis:	Artificial neural network (ANN) is a set of units in processing a model based on the habits of human neural networks. ANN has one of its duties, namely classification with the concept of supervised learning. ANN also has various methods in performing its duties such as Multilayer perceptron (MLP). Where MLP is one of the ANN methods that can classify based on data as conceptualized in data mining. Very useful classifications and trends in the field of research because of the review of data that will generate knowledge. Nominal Exchange Rate is one of the datasets tested in this study. The exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) is very necessary both in terms of stock movements and other businesses. So that, it is necessary to use classification to predict future exchange rates. In this study, the MLP method was carried out by obtaining a validation test using MAPE based on the detection rate of sebesar 0.500079879%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7b2f4b033f7428a790001d7a20b4890b https://doi.org/10.31289/jite.v5i1.5252 Zobrazit plný text záznamu