Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Asep Saepulrohman, Shandy Zolla Pratama

Judul : Prediksi Arah Harga Bitcoin Berdasarkan Manipulasi Metode Long Short-Term Memory (LSTM)

Abstrak :
Bitcoin is one of the new currencies in online transactions that was found to be one of the cryptocurrencies that has fluctuating volatility, so researchers are looking for the determinants of bitcoin price. In this paper, we adopt a Long Short-Term Memory Networks (LSTM) approach to evaluate the effect of investor attention on bitcoin returns by constructing an aggregate investor attention proxy. In addition, LSTM is a method of Recurrent Neural Network (RNN) to deal with the problem of long-term dependence on RNN. The main objective of this research is to design, implement, and visualize Bitcoin Price Prediction using the LSTM method. The data this study are daily bitcoin cover data and tweets taken from twitter. The measure of the accuracy of the model used is the Root Mean Square Error (RMSE). The results show that the RMSE value is quite small, meaning that it is quite good in modeling bitcoin predictions.

Tahun : 2023 Media Publikasi : Jurnal Nasional Blm Akreditasi

Kategori : Jurnal No/Vol/Tahun : 1 / 3 / 2023

ISSN/ISBN : 2809-3755

PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :
[1] Koo, E., & Kim, G. (2021). Prediction of Bitcoin price based on manipulating distribution strategy. Applied Soft Computing, 110, 107738.

[2] Mitsuru Iwamura, Yukinobu Kitamura, Tsutomu Matsumoto, Kenji Saito, Can we stabilize the price of a cryptocurrency?: Understanding the design of Bitcoin and its potential to compete with central bank money, Hitotsubashi J. Econ. (2019) 41–60.

[3] David Yermack, Is Bitcoin a real currency? An economic appraisal, in: Handbook of Digital Currency, Elsevier, 2015, pp. 31–43.

[4] Dirk G. Baur, Kihoon Hong, Adrian D. Lee, Bitcoin: Medium of exchange or speculative assets? J. Int. Financ. Mark. Inst. Money 54 (2018) 177–189.

[5] Vladimir Stojanovic, Dragan Prsic, Robust identification for fault detection in the presence of non-Gaussian noises: application to hydraulic servo drives, Nonlinear Dynam. 100 (2020) 2299–2313.

[6] S. Wahyudi and C. Imron, “Super Edge-Magic Pada[1] Faadilah, A. 2020. Analisis Sentimen Pada Ulasan Aplikasi Tokopedia di Google Play Store Menggunakan Metode Long Short Term Memory. Skripsi. Jakarta: UIN Syarif Hidayatullah Jakarta.

[7] Fariska, P., Nugraha, & Rohandi, M. M. A. 2020. Hubungan Sentimen Investor, Volume Perdagangan dan Kebijakan Moneter Pada Perkembangan Pasar Modal di Indonesia. Jurnal Manajemen dan Bisnis, 17(1) e-ISSN 2599-0039.

[8] Fatah, H., Subekti, A., 2018. Prediksi Harga Cryptocurrency Dengan Metode K-Nearest Neighbours. Jurnal PILAR Nusa Mandiri Vol. 14, No. 2. P-ISSN: 1978-1946, E-ISSN: 2527-6514.

[9] Ilmawan, D., Warsito, B., & Sugito, S. 2020. Penerapan Artificial Neural Network Dengan Optimasi Modified Artificial Bee Colony Untuk Meramalkan Harga Bitcoin Terhadap Rupiah. Jurnal Gaussian, 9(2), 135-142.

[10] Juanda, R. A., Jondri, J., & Rohmawati, A. A. 2018. Prediksi Harga Bitcoin Dengan Menggunakan Recurrent Neural Network. eProceedings of Engineering, 5(2).

[11] Le, X. H., Ho, H. V., Lee, G., & Jung, S. 2019. Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11(7), 1387.

[12] Mardi, Y. 2017. Data Mining: Klasifikasi Menggunakan Algoritma C4.5. Edik Informatika, 2(2), 213-219.

[13] Mehta, P., Sasikala, E. 2020. Prediction of Bitcoin using Recurrent Neural Network. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878 (Online), Volume-8 Issue-6.

[14] Nurdiansyah, A., Furqon, M. T., & Rahayudi, B. 2019. Prediksi Harga Bitcoin Menggunakan Metode Extreme Learning Machine (ELM) dengan Optimasi Artificial Bee Colony (ABC). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X. [10] A. Rorres, An Introduction to Linear Algebra, 4 Edition. London: Pearson, 2017.

[15] Rohmawati, F., Rohman, M. G., & Mujilahwati, S. 2017. Sistem Prediksi Jumlah Pengunjung Wisata Wego Kec. Sugio Kab. Lamongan Menggunakan Metode Fuzzy Time Series. Joutica, 2(2).

[16] Salwa, N., Tatsara, N., Amalia, R., & Zohra, A. F. 2018. Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 21-31.

[17] Turban, E., Delen, D., & Sharda, R. 2018. Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Harlow ; Munich: Pearson Prentice Hall.

[18] Winata, W. 2018. Prakira Suhu Udara Rata-Rata Kota-Kota Besar Dunia Menggunakan Metode Long Short-Term Memory. Skripsi. Medan: Universitas Sumatera Utara.

[19] Wiranda, L., & Sadikin, M. 2020. Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 8(3), 184-196.

URL : https://journal.unpak.ac.id/index.php/intv/article/view/7290/4257

Document

back

@official_unpak	@unpak
@unpak	UNPAK TV

Welcome Repository Universitas Pakuan

Student's Achievments

Unggul, Mandiri & Berkarakter

Detail Karya Ilmiah Dosen

Asep Saepulrohman, Shandy Zolla Pratama

Judul	:	Prediksi Arah Harga Bitcoin Berdasarkan Manipulasi Metode Long Short-Term Memory (LSTM)
Abstrak	:	Bitcoin is one of the new currencies in online transactions that was found to be one of the cryptocurrencies that has fluctuating volatility, so researchers are looking for the determinants of bitcoin price. In this paper, we adopt a Long Short-Term Memory Networks (LSTM) approach to evaluate the effect of investor attention on bitcoin returns by constructing an aggregate investor attention proxy. In addition, LSTM is a method of Recurrent Neural Network (RNN) to deal with the problem of long-term dependence on RNN. The main objective of this research is to design, implement, and visualize Bitcoin Price Prediction using the LSTM method. The data this study are daily bitcoin cover data and tweets taken from twitter. The measure of the accuracy of the model used is the Root Mean Square Error (RMSE). The results show that the RMSE value is quite small, meaning that it is quite good in modeling bitcoin predictions.
Tahun	:	2023	Media Publikasi	:	Jurnal Nasional Blm Akreditasi
Kategori	:	Jurnal	No/Vol/Tahun	:	1 / 3 / 2023
ISSN/ISBN	:	2809-3755
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	[1] Koo, E., & Kim, G. (2021). Prediction of Bitcoin price based on manipulating distribution strategy. Applied Soft Computing, 110, 107738. [2] Mitsuru Iwamura, Yukinobu Kitamura, Tsutomu Matsumoto, Kenji Saito, Can we stabilize the price of a cryptocurrency?: Understanding the design of Bitcoin and its potential to compete with central bank money, Hitotsubashi J. Econ. (2019) 41–60. [3] David Yermack, Is Bitcoin a real currency? An economic appraisal, in: Handbook of Digital Currency, Elsevier, 2015, pp. 31–43. [4] Dirk G. Baur, Kihoon Hong, Adrian D. Lee, Bitcoin: Medium of exchange or speculative assets? J. Int. Financ. Mark. Inst. Money 54 (2018) 177–189. [5] Vladimir Stojanovic, Dragan Prsic, Robust identification for fault detection in the presence of non-Gaussian noises: application to hydraulic servo drives, Nonlinear Dynam. 100 (2020) 2299–2313. [6] S. Wahyudi and C. Imron, “Super Edge-Magic Pada[1] Faadilah, A. 2020. Analisis Sentimen Pada Ulasan Aplikasi Tokopedia di Google Play Store Menggunakan Metode Long Short Term Memory. Skripsi. Jakarta: UIN Syarif Hidayatullah Jakarta. [7] Fariska, P., Nugraha, & Rohandi, M. M. A. 2020. Hubungan Sentimen Investor, Volume Perdagangan dan Kebijakan Moneter Pada Perkembangan Pasar Modal di Indonesia. Jurnal Manajemen dan Bisnis, 17(1) e-ISSN 2599-0039. [8] Fatah, H., Subekti, A., 2018. Prediksi Harga Cryptocurrency Dengan Metode K-Nearest Neighbours. Jurnal PILAR Nusa Mandiri Vol. 14, No. 2. P-ISSN: 1978-1946, E-ISSN: 2527-6514. [9] Ilmawan, D., Warsito, B., & Sugito, S. 2020. Penerapan Artificial Neural Network Dengan Optimasi Modified Artificial Bee Colony Untuk Meramalkan Harga Bitcoin Terhadap Rupiah. Jurnal Gaussian, 9(2), 135-142. [10] Juanda, R. A., Jondri, J., & Rohmawati, A. A. 2018. Prediksi Harga Bitcoin Dengan Menggunakan Recurrent Neural Network. eProceedings of Engineering, 5(2). [11] Le, X. H., Ho, H. V., Lee, G., & Jung, S. 2019. Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11(7), 1387. [12] Mardi, Y. 2017. Data Mining: Klasifikasi Menggunakan Algoritma C4.5. Edik Informatika, 2(2), 213-219. [13] Mehta, P., Sasikala, E. 2020. Prediction of Bitcoin using Recurrent Neural Network. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878 (Online), Volume-8 Issue-6. [14] Nurdiansyah, A., Furqon, M. T., & Rahayudi, B. 2019. Prediksi Harga Bitcoin Menggunakan Metode Extreme Learning Machine (ELM) dengan Optimasi Artificial Bee Colony (ABC). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X. [10] A. Rorres, An Introduction to Linear Algebra, 4 Edition. London: Pearson, 2017. [15] Rohmawati, F., Rohman, M. G., & Mujilahwati, S. 2017. Sistem Prediksi Jumlah Pengunjung Wisata Wego Kec. Sugio Kab. Lamongan Menggunakan Metode Fuzzy Time Series. Joutica, 2(2). [16] Salwa, N., Tatsara, N., Amalia, R., & Zohra, A. F. 2018. Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 21-31. [17] Turban, E., Delen, D., & Sharda, R. 2018. Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Harlow ; Munich: Pearson Prentice Hall. [18] Winata, W. 2018. Prakira Suhu Udara Rata-Rata Kota-Kota Besar Dunia Menggunakan Metode Long Short-Term Memory. Skripsi. Medan: Universitas Sumatera Utara. [19] Wiranda, L., & Sadikin, M. 2020. Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 8(3), 184-196.
URL	:	https://journal.unpak.ac.id/index.php/intv/article/view/7290/4257