Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS
Keywords:
Cryptocurrency, Bitcoin, K-Nearest Neighbours
Abstract
Electronic money is getting more popular as online transaction means among people especially for entrepreneurs, businessmen and investors due to its practicality. Cryptocurrency emerges as the solution to resolve the contrains of electronic money that depends heavily on third parties. One of widely used Cryptocurrency is Bitcoin. The price of bitcoin fluctuates in very short duration. It is similar with the fluctuation of stock price in stock market. Prediction for future price becomes important anad interesting. In this paper, we propose a prediction model for cryptocurrency price. Out proposed model predict the cryptocurreny price using KNN (K-Nearest Neighbors) method. With the parameter of k=3 and using linear NN search algorithm, out proposed method gives a mean absolute error (MAE) of 0.0018 and root mean squared error (RMSE) of 0.0089.
Downloads
Download data is not yet available.
References
Alkhatib, K., Najadat, H., Hmeidi, I., & Shatnawi, M. K. A. (2013). Stock Price Prediction Using k-Nearest Neighbor (kNN) Algorithm. Nternational Journal of Business, Humanities and Technology 3, 3(3), 32–44.
Banjarsari, M. A., Budiman, I., & Farmadi, A. (2015). PENERAPAN K-OPTIMAL PADA ALGORITMA KNN UNTUK PREDIKSI KELULUSAN TEPAT WAKTU MAHASISWA PROGRAM STUDI ILMU KOMPUTER FMIPA UNLAM BERDASARKAN IP SAMPAI DENGAN SEMESTER 4. KLIK - KUMPULAN JURNAL ILMU KOMPUTER, 2(2), 159–173. https://doi.org/10.20527/KLIK.V2I2.26
Claudy, Y. I., Perdana, R. S., & Fauzi, M. A. (2018). Klasifikasi Dokumen Twitter Untuk Mengetahui Karakter Calon Karyawan Menggunakan Algoritme K-Nearest Neighbor (KNN). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2761–2765. Retrieved from https://www.researchgate.net/publication/322959490
Dourado, E., & Brito, J. (2014). Cryptocurrency. The New Palgrave Dictionary of Economics (Online).
Fakhruddin, H. M., & Darmadji, T. (2011). Pasar Modal Di Indonesia, Pendekatan Tanya Jawab (3rd ed.). Jakarta: Salemba 4.
Fatah, H., & Subekti, A. (2018). Laporan Akhir Penelitian - Prediksi Harga Cryptocurrency Dengan Metode K-Nearest Neighbours. Jakarta.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Berlin, Heidelberg: Springer Science & Business Media. https://doi.org/10.1007/978-3-642-19721-5
Leidiyana, H. (2013). Penerapan algoritma k-nearest neighbor untuk penentuan resiko kredit kepemilikan kendaraan bemotor. Jurnal Penelitian Ilmu Komputer, System Embedded & Logic, 1(1), 65–76.
Mulyanto, F. (2015). Pemanfaatan Cryptocurrency Sebagai Penerapan Mata Uang Rupiah Kedalam Bentuk Digital Menggunakan Teknologi Bitcoin. IJNS - Indonesian Journal on Networking and Security, 4(4), 19–26. https://doi.org/10.1123/IJNS.V4I4.1364
Setyowati, R. A. D. (2013). Penerapan Fuzzy Iterative Dichotomiser 3 (Fuzzy ID3) Pada Data Fluktuasi Harga Saham. Universitas Brawijaya Malang.
Sinha, P., & Sinha, P. (2015). Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM. International Journal of Engineering Research & Technology (IJERT), 4(12), 608–612. Retrieved from www.ijert.org
Banjarsari, M. A., Budiman, I., & Farmadi, A. (2015). PENERAPAN K-OPTIMAL PADA ALGORITMA KNN UNTUK PREDIKSI KELULUSAN TEPAT WAKTU MAHASISWA PROGRAM STUDI ILMU KOMPUTER FMIPA UNLAM BERDASARKAN IP SAMPAI DENGAN SEMESTER 4. KLIK - KUMPULAN JURNAL ILMU KOMPUTER, 2(2), 159–173. https://doi.org/10.20527/KLIK.V2I2.26
Claudy, Y. I., Perdana, R. S., & Fauzi, M. A. (2018). Klasifikasi Dokumen Twitter Untuk Mengetahui Karakter Calon Karyawan Menggunakan Algoritme K-Nearest Neighbor (KNN). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2761–2765. Retrieved from https://www.researchgate.net/publication/322959490
Dourado, E., & Brito, J. (2014). Cryptocurrency. The New Palgrave Dictionary of Economics (Online).
Fakhruddin, H. M., & Darmadji, T. (2011). Pasar Modal Di Indonesia, Pendekatan Tanya Jawab (3rd ed.). Jakarta: Salemba 4.
Fatah, H., & Subekti, A. (2018). Laporan Akhir Penelitian - Prediksi Harga Cryptocurrency Dengan Metode K-Nearest Neighbours. Jakarta.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Berlin, Heidelberg: Springer Science & Business Media. https://doi.org/10.1007/978-3-642-19721-5
Leidiyana, H. (2013). Penerapan algoritma k-nearest neighbor untuk penentuan resiko kredit kepemilikan kendaraan bemotor. Jurnal Penelitian Ilmu Komputer, System Embedded & Logic, 1(1), 65–76.
Mulyanto, F. (2015). Pemanfaatan Cryptocurrency Sebagai Penerapan Mata Uang Rupiah Kedalam Bentuk Digital Menggunakan Teknologi Bitcoin. IJNS - Indonesian Journal on Networking and Security, 4(4), 19–26. https://doi.org/10.1123/IJNS.V4I4.1364
Setyowati, R. A. D. (2013). Penerapan Fuzzy Iterative Dichotomiser 3 (Fuzzy ID3) Pada Data Fluktuasi Harga Saham. Universitas Brawijaya Malang.
Sinha, P., & Sinha, P. (2015). Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM. International Journal of Engineering Research & Technology (IJERT), 4(12), 608–612. Retrieved from www.ijert.org
Published
2018-09-15
How to Cite
Fatah, H., & Subekti, A. (2018). PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS. Jurnal Pilar Nusa Mandiri, 14(2), 137-144. https://doi.org/10.33480/pilar.v14i2.30
Article Metrics
Abstract viewed = 1261 times
PDF downloaded = 2961 times
Abstract viewed = 1261 times
PDF downloaded = 2961 times
Section
Articles
Copyright (c) 2018 Haerul Fatah & Agus Subekti
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
