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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Yusuf Irianto, Suhendro
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Computer Science & IT

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Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction Apriadi, Eko Aziz; Sriyanto; Lestari, Sri; Yusuf Irianto, Suhendro
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13418

Abstract

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments
Co-Authors Apriadi, Eko Aziz Sri Lestari Sriyanto