Prediksi Harga Cryptocurrency Binance Berdasarkan Informasi Blokchain dengan Menggunakan Algoritma Random Forest

 (*)Jumjumi Asbullah Mail (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)
 Samsudin Samsudin (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)

(*) Corresponding Author

Submitted: December 8, 2023; Published: January 10, 2024

Abstract

This study proposes the use of the Random Forest algorithm to forecast the cryptocurrency Binance's daily prices. With a dataset covering 1992 observations from January 1, 2018, to June 15, 2023, the research focuses on PT. Tennet Depository Indonesia. Through Python implementation, the experimental results indicate that Random Forest is effective in providing accurate price predictions, with an average Mean Absolute Percentage Error (MAPE) of approximately 1.38% and an average Root Mean Squared Error (RMSE) of about 4.38. The uniqueness of the system lies in the algorithm's capability to handle market complexities and volatility, offering adaptive solutions to the unpredictable dynamics of the market. Nevertheless, limitations in historical data and market volatility persist as inhibiting factors, emphasizing the need for a holistic approach. The average MAPE and RMSE results provide an indication of the overall reliability of the model in facing cryptocurrency market volatility. These conclusions can contribute to the development of more robust and adaptive models to respond to the evolving market conditions.

Keywords


Random Forest; Price Prediction; Cryptocurrency; Market Dynamics

Full Text:

PDF


Article Metrics

Abstract view : 593 times
PDF - 418 times

References

A. Jumjumi and T. Triase, Rancang dan Bangun Aplikasi Helpdesk Support System berbasis website pada CV. Gyar Indonesia, J. Ilm. Betrik (Besemah Teknol. Inf. dan Komputer), vol. 14, no. 01, pp. 195204, 2023, doi: 10.36050/betrik.v14i01%20APRIL.26.

B. A. Romadhoni, Meredupnya Media Cetak, Dampak Kemajuan Teknologi Informasi, An-Nida J. Komun. Islam, vol. 10, no. 1, pp. 1320, Jul. 2019, doi: 10.34001/an.v10i1.741.

A. H. Al-Nefaie and T. H. H. Aldhyani, Bitcoin Price Forecasting and Trading: Data Analytics Approaches, Electronics, vol. 11, no. 24, pp. 118, Dec. 2022, doi: 10.3390/electronics11244088.

M. Faghih Mohammadi Jalali and H. Heidari, Predicting changes in Bitcoin price using grey system theory, Financ. Innov., vol. 6, no. 1, pp. 112, Dec. 2020, doi: 10.1186/s40854-020-0174-9.

R. Sholeh and K. Huda, Pengaruh Kemajuan Teknologi Terhadap Volume Pejualan Ritel Di Kota Mojokerto, J. OPTIMA, vol. 3, no. 1, pp. 8090, Apr. 2019, doi: 10.33366/optima.v3i1.1253.

D. Aggarwal, S. Chandrasekaran, and B. Annamalai, A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices, J. Behav. Exp. Financ., vol. 27, pp. 112, Sep. 2020, doi: 10.1016/j.jbef.2020.100335.

I. Nurhaida, M. Sobiri, and S. Jaya, Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning, JSAI (Journal Sci. Appl. Informatics), vol. 6, no. 2, pp. 197204, Jun. 2023, doi: 10.36085/jsai.v6i2.5288.

S. Erfanian, Y. Zhou, A. Razzaq, A. Abbas, A. A. Safeer, and T. Li, Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach, Entropy, vol. 24, no. 10, pp. 129, Oct. 2022, doi: 10.3390/e24101487.

H.-M. Kim, G.-W. Bock, and G. Lee, Predicting Ethereum prices with machine learning based on Blockchain information, Expert Syst. Appl., vol. 184, pp. 18, Dec. 2021, doi: 10.1016/j.eswa.2021.115480.

B. Agarwal, P. Harjule, L. Chouhan, U. Saraswat, H. Airan, and P. Agarwal, Prediction of dogecoin price using deep learning and social media trends, EAI Endorsed Trans. Ind. Networks Intell. Syst., vol. 8, no. 29, pp. 112, Nov. 2021, doi: 10.4108/eai.29-9-2021.171188.

V. Derbentsev, V. Babenko, K. Khrustalev, H. Obruch, and S. Khrustalova, Comparative Performance of Machine Learning Ensemble Algorithms for Forecasting Cryptocurrency Prices, Int. J. Eng., vol. 34, no. 01, pp. 140148, 2021, doi: 10.5829/ije.2021.34.01a.16.

S. Tanwar, N. P. Patel, S. N. Patel, J. R. Patel, G. Sharma, and I. E. Davidson, Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations, IEEE Access, vol. 9, pp. 138633138646, 2021, doi: 10.1109/ACCESS.2021.3117848.

A. AKGL, E. E. ?AH?N, and F. Y. ?ENOL, Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis, Chaos Theory Appl., vol. 4, no. 3, pp. 157168, Nov. 2022, doi: 10.51537/chaos.1199241.

F. Ferdiansyah, S. H. Othman, R. Z. Md Radzi, D. Stiawan, and T. Sutikno, Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy, IAES Int. J. Artif. Intell., vol. 12, no. 1, p. 251, Mar. 2023, doi: 10.11591/ijai.v12.i1.pp251-261.

D. Adi Nugroho and R. Setiawan, Factors For Changes in Trading Volume, Changes in Market Capitalization, and Changes in Circulating Supply to Binance (BNB) Returns, J. Ekon., vol. 12, no. 02, pp. 10561065, 2023, [Online]. Available: http://ejournal.seaninstitute.or.id/index.php/Ekonomi

S. Saadah and H. Salsabila, Prediksi Harga Bitcoin Menggunakan Metode Random Forest (Studi Kasus: Data Acak Pada Awal Masa Pandemic Covid-19), J. Komput. Terap., vol. 7, no. 1, pp. 2432, Jun. 2021, doi: 10.35143/jkt.v7i1.4618.

M. A. Ammer and T. H. H. Aldhyani, Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness, Electronics, vol. 11, no. 15, pp. 122, Jul. 2022, doi: 10.3390/electronics11152349.

M. S. Islam, E. Hossain, A. Rahman, M. S. Hossain, and K. Andersson, A Review on Recent Advancements in FOREX Currency Prediction, Algorithms, vol. 13, no. 8, pp. 123, Jul. 2020, doi: 10.3390/a13080186.

Z. Ye, Y. Wu, H. Chen, Y. Pan, and Q. Jiang, A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin, Mathematics, vol. 10, no. 8, pp. 121, Apr. 2022, doi: 10.3390/math10081307.

M. A. Maliki, I. Cholissodin, and N. Yudistira, Prediksi Pergerakan Harga Cryptocurrency Bitcoin terhadap Mata Uang Rupiah menggunakan Algoritme LSTM, J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 7, pp. 32593268, 2022, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/11326

D. V Firsov, S. N. Silvestrov, N. V. Kuznetsov, E. V. Zolotarev, and S. A. Pobyvaev, Using PPO Models to Predict the Value of the BNB Cryptocurrency, Emerg. Sci. J., vol. 7, no. 4, pp. 12061214, Jul. 2023, doi: 10.28991/ESJ-2023-07-04-012.

Moch Farryz Rizkilloh and Sri Widiyanesti, Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM), J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 2531, 2022, doi: 10.29207/resti.v6i1.3630.

A. A. Ashara, A. Muttaqin Mustari, and M. Idris, Implementation Of Government Organizational Communications At The Regional Development Planning, Research And Development Agency In Improving The Performance Of State Civil Apparatus In South Sulawesi Province, Respon J. Ilm. Mhs. Ilmu Komun., vol. 3, no. 2, pp. 177187, 2022, doi: 10.33096/respon.v3i2.117.

M. Farryz Rizkilloh and S. Widiyanesti, Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM), J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 2531, Feb. 2022, doi: 10.29207/resti.v6i1.3630.

K. Kasliono, N. Candraningrum, and K. Sari, Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine, Build. Informatics, Technol. Sci., vol. 5, no. 1, Jun. 2023, doi: 10.47065/bits.v5i1.3567.

M. N. Pangestu, M. Jajuli, and U. Enri, Prediksi Harga Kartu Grafis NVIDIA Berdasarkan Pengaruh Harga Cryptocurrency Menggunakan Support Vector Regression, J. Ilm. Wahana Pendidik., vol. 8, no. 17, pp. 280287, 2022, doi: https://doi.org/10.5281/zenodo.7076540.

M. K. Anam and D. A. Jakaria, Sistem Prediksi Harga Kripto Dengan Metode Regresi, J. Tek. Inform. dan Sist. Inf., vol. 10, no. 2, pp. 467479, 2023, doi: https://doi.org/10.35957/jatisi.v10i2.4787.

M. F. Arfa, M. R. AlFathan, H. B. Lumbantobing, and R. Rahmadenni, Prediksi Harga Cryptocurrency Dengan Metode Linier Regresi, SENTIMAS Semin. Nas. Penelit. dan Pengabdi. Masy., vol. 1, no. 1, pp. 815, 2023, [Online]. Available: https://journal.irpi.or.id/index.php/sentimas/article/view/609

A. H. A. Othman, S. Kassim, R. Bin Rosman, and N. H. B. Redzuan, Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach, J. Revenue Pricing Manag., vol. 19, no. 5, pp. 314330, Oct. 2020, doi: 10.1057/s41272-020-00229-3.

N. Fajriati and B. Prasetiyo, Optimasi Algoritma Nave Bayes Dengan Diskritisasi K-Means Pada Diagnosis Penyakit Jantung, J. Teknol. Inf. dan Ilmu Kompter, vol. 10, no. 3, pp. 503512, 2023, doi: 10.25126/jtiik.2023106510.

A. Yunizar, T. Rismawan, and D. M. Midyanti, Penerapan Metode Recurrent Neural Network Model Gated Recurrent Unit Untuk Rediksi Harga Cryptocurrency, Coding J. Komput. dan , vol. 11, no. 1, pp. 3241, 2023, doi: http://dx.doi.org/10.26418/coding.v11i1.58073.

P. A. Raharja, Prediksi Harga Ethereum Menggunakan Metode Vector Autoregressive, J. Informatics, Inf. Syst. Softw. Eng. Appl., vol. 3, no. 2, pp. 7179, 2021, doi: https://doi.org/10.20895/inista.v3i2.285.

R. Faizal, B. D. Setiawan, and I. Cholisoddin, Prediksi Nilai Cryptocurrency Bitcoin menggunakan Algoritme Extreme Learning Machine (ELM), J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 5, pp. 42264233, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5170

I. Indriyanti, N. Ichsan, H. Fatah, T. Wahyuni, and E. Ermawati, Implementasi Orange Data Mining Untuk Prediksi Harga Bitcoin, J. Responsif Ris. Sains dan Inform., vol. 4, no. 2, pp. 118125, Aug. 2022, doi: 10.51977/jti.v4i2.762.

P. K. Arieska and N. Herdiani, Pemilihan Teknik Sampling Berdasarkan Perhitungan Efisiensi Relatif, J. Stat. Univ. Muhammadiyah Semarang, vol. 6, no. 2, pp. 166171, 2018, doi: 10.26714/jsunimus.6.2.2018.%25p.

D. Denisko and M. M. Hoffman, Classification and interaction in random forests, J. Int. PNAS, pp. 1012, 2018, doi: 10.1073/pnas.1800256115.

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

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


JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

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