COMPARISON OF DATA MINING ALGORITHM FOR FORECASTING BITCOIN CRYPTO CURRENCY TRENDS
The popularity of cryptocurrencies has been increasing in the approximately 10 years since their emergence in 2008. Bitcoin is the most popular and the most instrumental in the existence of cryptocurrencies. The price of coins in cryptocurrencies is the same as the price of shares in the capital market which always fluctuates and even tends to be more volatile than the stock market. This condition is very influential for actors in cryptocurrencies. This study aims to compare the Algorithm Forecasting so that it can be known the right algorithm in Forecasting the trend of Bitcoin. The algorithm used is Algorithm Supervised Learning that is Neural Network, Linear Regression, Support Vector Machine, Gaussian Process, and polynomial Regression. Accuracy was measured using a 10 Fold Cross-validation model and evaluation is done by Root Mean Square Error (RMSE). The results showed that the Algorithm Neural Network is an Algorithm Forecasting best with RMSE value 277,237 +/- 74,736 (micro: 287,208 +/- 0.000) among other Algorithms so that Neural Network can be used for Forecasting cryptocurrency Bitcoin.
R. C. Noorsanti, H. Yulianton, and K. Hadiono, “Blockchain - Teknologi Mata Uang Cryptocurrency,” Pros. SENDI_U 2018, pp. 306–311, 2018.
A. P. Singh and S. Malani, “Understanding and Predicting Trends In Cryptocurrency Prices Using Data Mining Techniques,” IIIT Hyderabad, pp. 1–7, 2018.
D. T. Larose and C. D. Larose, Discovering Knowledge In Data An Introduction to Data Mining Second Edition Wiley Series on Methods and Applications in Data Mining. Canada: John Wiley & Sons, Inc, 2014.
C. C. Aggarwal, Data Mining : The Textbook. New York: Springer, 2015.
M. Ardiansyah Sembiring, M. Fitri Larasati Sibuea, and A. Sapta, “Analisa Kinerja Algoritma C.45 Dalam Memprediksi Hasil Belajar,” J. Sci. Soc. Res., vol. 1, no. February, pp. 73–79, 2018, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR.
A. A. Argasah and D. Gustian, “Data Mining Analysis To Determine Employee Salaries According To Needs Based On The K-Medoids Clustering Algorithm Analisis Data Mining Untuk Menentukan Gaji Karyawan Sesuai Penilaian Kemampuan Menggunakan Algoritma K-Medoids,” JUTIF, vol. 3, no. 1, pp. 29–35, 2022.
A. R. Muhajir, E. Sutoyo, and I. Darmawan, “Forecasting Model Penyakit Demam Berdarah Dengue Di Provinsi DKI Jakarta Menggunakan Algoritma Regresi Linier Untuk Mengetahui Kecenderungan Nilai Variabel Prediktor Terhadap Peningkatan Kasus,” Fountain Informatics J., vol. 4, no. 2, pp. 33–40, 2019.
R. Maulana and D. Kumalasari, “Analisis Dan Perbandingan Algoritma Data Mining Dalam Prediksi Harga Saham GGRM,” J. Inform. Kaputama, vol. 3, no. 1, pp. 22–28, 2019, [Online]. Available: https://finance.yahoo.com/quote/GGRM.J.
R. H. Kusumodestoni and Sarwido, “Komparasi Model Support Vector Machines (SVM) Dan Neural Network Untuk Mengetahui Tingkat Akurasi Prediksi Tertinggi Harga Saham,” J. Inform. UPGRIS, vol. 3, no. 1, pp. 1–9, 2017, doi: 10.26877/jiu.v3i1.1536.
P. Wahyuningtias, H. W. Utami, U. A. Raihan, and H. N. Hanifah, “Comparison Of Random Forest And Support Vector Machine Methods On Twitter Sentiment Analysis ( Case Study : Internet Selebgram Rachel Vennya Escape From Quarantine ) Perbandingan Metode Random Forest Dan Support Vector Machine Pada Analisis Sentimen Twitt,” JUTIF, vol. 3, no. 1, pp. 141–145, 2022.
S. Kumar, “Cryptocurrency Historical Prices Dataset,” Kaggle, 2018. https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory/%0Aversion/13.
W. Baswardono, D. Kurniadi, A. Mulyani, and D. M. Arifin, “Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification,” J. Phys. Conf. Ser., vol. 1402, no. 6, pp. 1–6, 2019, doi: 10.1088/1742-6596/1402/6/066055.
I. H. Witten, E. Frank, and M. A. Hall, Data Mining Practical Machine Learning Tools and Technique. San Francisco: Morgan Kaufmann, 2011.
N. Ye, Data Mining: Theories, Algorithms, and Examples. New York: Taylor & Francis Group, 2014.
Han and Kamber, Data Mining Concepts and Technique. San Francisco: Diane Cerra, 2006.
K. Fatmawati and A. P. Windarto, “Data Mining : Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue ( DBD ) Berdasarkan Provinsi,” CESS (Journal Comput. Eng. Syst. Sci., vol. 3, no. 2, pp. 173–178, 2018.
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