Improving Algorithm Performance using Feature Extraction for Ethereum Forecasting

Improving Algorithm Performance using Feature Extraction for Ethereum Forecasting

algorithms;ethereum;feature extraction;forecasting

Ethereum is a cryptocurrency that is now the second most popular digital asset after Bitcoin. Hightrading volume is the trigger for the popularity of this cryptocurrency. In addition, Ethereum is home to various decentralized applications and acts as a link for Decentralized Finance (DeFi) transactions, Non-Fungible Tokens (NFTs) and the use of smart contracts in the crypto space. This study aims to improve the performance of the forecasting algorithm by using Feature Extraction for Ethereum price forecasting. The algorithms used are Neural Networks, Deep Learning and Support Vector Machines. The research methodology used is Knowledge Discovery in Databases. The dataset used comes from the yahoo.finance.com website regarding Ethereum prices.The research results indicated that the use of Feature Extraction improved the performance of the constructed model. The results show that the Neural Network Algorithm is the best Algorithm compared to Deep Learning and Support Vector Machine. The Root Mean Square Error value for the Neural Network before Feature Selection is 93,248 +/-168,135 (micro average: 186,580 +/-0,000) Linear Sampling method and 54,451 +/-26,771 (micro average: 60,318 +/-0,000) Shuffled Sampling method. Then after the Feature Selection, the Root Mean Square Error value improved to 38,102 +/-31,093 (micro average: 48,600 +/-0,000) usingthe Shuffled Sampling method.This research bridged the gap by either expanding on prior studies or contributing through the comparison of three forecasting algorithms for cryptocurrency datasets. It also compared two feature extraction algorithms, namelyPrincipal Component Analysis and Independent Component Analysis, and employed the T-Test to conduct a performance difference analysis among algorithm results to determine the best model performance

Indri Tri Julianto1, Dede Kurniadi2, Ricky Rohmanto3, Fathia Alisha Fauzia4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/4872/895

1,2Departmentof Computer Science,Institut Teknologi Garut,Indonesia3DepartmentofInformatic Management, Universitas Ma’some,Bandung,Indonesia4DepartmentofCommunication and Information, Universitas Garut,Indonesia

15-02-2024

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