<![CDATA[Bitcoin is the most popular cryptocurrency currently being favored as a means of investment like stocks. Its nature is not centralized or decentralized which causes the price of Bitcoin can experience inflation at any time. So we need a method to predict the price of Bitcoin accurately to make decisions in Bitcoin buying and selling transactions. The ELM method has better learning speed than other methods and a simple structure, but it has disadvantages in choosing input weights and biases randomly. To overcome these shortcomings, the ABC method is used because it also has a very simple and flexible structure. Therefore, the price of Bitcoin will be predicted using the ELM-ABC method. This research uses Bitcoin price time series data from the Indodax cryptocurrency exchange from 01 December 2017 to 31 August 2018. ABC functions to produce the most optimal input weights and biases for the ELM training stage. Furthermore, input weights, biases, and output weights will be used for ELM testing stages to obtain the prediction result prices. Then, error evaluation value calculated from the results of the Bitcoin price prediction using MAPE. The ELM-ABC parameter test results get the best combination of 12 features, 20 hidden neurons, 20 bee populations, and 5 iterations. The combination produces an average MAPE value of 1,96983% and an accuracy of 98,03017%, while ELM amounted to 2,70401% and 97,29599%.]]>
Copyrights © 2019
