Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction

Authors

  • Eko Aziz Apriadi Institut Informatika dan Bisnis Darmajaya
  • Sriyanto Institut Informatika dan Bisnis Darmajaya
  • Sri Lestari Institut Informatika dan Bisnis Darmajaya
  • Suhendro Yusuf Irianto Institut Informatika dan Bisnis Darmajaya

DOI:

10.33395/sinkron.v8i2.13418

Keywords:

K-Nearest Neighbors (K-NN), Neural Network, Bitcoin Price Prediction, Root Mean Square Error (RSME), Mean Absolute Error (MAE)

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

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How to Cite

Apriadi, E. A., Sriyanto, Lestari, S. ., & Yusuf Irianto, S. (2024). Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 617-622. https://doi.org/10.33395/sinkron.v8i2.13418

Issue

Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024

Section

Articles

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Copyright (c) 2024 Eko Aziz Apriadi, Sriyanto, Sri Lestari, Suhendro Yusuf Irianto

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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