Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm
Keywords:
Sentiment analysis, Bitcoin, Hybrid model, Convolutional Neural Network, Long Short Term Memory
Abstract
Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.
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References
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[2] I. G. H. Saputra and I. D. P. S. Wardana, “Perlindungan Hukum Terhadap Masyarakat Pengguna Sistem Pembayaran Bitcoin dan Investasi Bitcoin di Indonesia ditinjau dari Hukum Perlindungan Konsumen,” J. Pacta Sunt Servanda, vol. 2, no. 2, pp. 24–35, 2021.
[3] A. K. Umam, O. H. P. Wardhana, and I. H. Hany, “Dinamika Cryptocurrency Dan Misi Ekonomi Islam,” An-Nisbah J. Ekon. Syariah, vol. 7, no. 2, pp. 366–386, 2020, doi: 10.21274/an.v7i02.3366.
[4] K. Fitriani, I. Isbandi, and A. Amaliyah, “Perancangan Sistem Manajemen Dokumen Dengan Menggunakan Metode Text Mining Di Kantor Kelurahan Sekejati,” Telematika, vol. 3, no. 1, pp. 45–59, 2021.
[5] A. Firdaus and W. I. Firdaus, “Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi : (Sebuah Ulasan),” J. JUPITER, vol. 13, no. 1, pp. 66–78, 2021.
[6] A. Z. Amrullah, A. Sofyan Anas, and M. A. J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” J. Bite, vol. 2, no. 1, pp. 40–44, 2020, doi: 10.30812/bite.v2i1.804.
[7] D. I. Af’idah, D. Dairoh, S. F. Handayani, R. W. Pratiwi, and S. I. Sari, “Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 3, pp. 607–618, 2022, doi: 10.30812/matrik.v21i3.1402.
[8] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 207–213, May 2020, doi: 10.30812/matrik.v19i2.640.
[9] H. Fonda, “Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn),” J. Ilmu Komput., vol. 9, no. 1, pp. 7–10, 2020, doi: 10.33060/jik/2020/vol9.iss1.144.
[10] M. Z. Ersyad, K. N. Ramadhani, and A. Arifianto, “Pengenalan Bentuk Tangan Dengan Convolutional Neural Network (Cnn),” eProceedings Eng., vol. 7, no. 2, pp. 8212–8222, 2020.
[11] P. A. Qori, D. S. Oktafani, and I. Kharisudin, “Analisis Peramalan dengan Long Short Term Memory pada Data Kasus Covid-19 di Provinsi Jawa Tengah,” in PRISMA, Prosiding Seminar Nasional Matematika, 2022, pp. 752–758.
[12] E. Irawati Setiawan, A. Ferdianto, J. Santoso, Y. Kristian, S. Sumpeno, and M. Hery Purnomo, “Analisis Pendapat Masyarakat terhadap Berita Kesehatan Indonesia menggunakan Pemodelan Kalimat berbasis LSTM (Indonesian Stance Analysis of Healthcare News using Sentence Embedding Based on LSTM),” J. Nas. Tek. Elektro dan Teknol. Inf. |, vol. 9, no. 1, pp. 8–17, 2020.
[13] R. W. Sandra, Y. Vitriani, M. Affandes, and S. Sanjaya, “Cryptocurrency Sentiment Classification Based on Comments On Facebook Using K-Nearest Neighbor,” Int. J. Inf. Syst. Technol. Akreditasi, vol. 6, no. 158, pp. 259–269, 2022.
[14] R. Azhar, A. Surahman, and C. Juliane, “Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes,” J. Sains Komput. Inform., vol. 6, no. 1, pp. 267–281, 2022.
[15] A. P. F. F. Y. N. K. E. S. N. W. Chandra, “Sentiment Analisis Terhadap Cryptocurrency Berdasarkan Comment Dan Reply Pada Platform Twitter,” J. Inf. Syst. Informatics, vol. 3, no. 2, pp. 268–277, 2021, [Online]. Available: http://journal-isi.org/index.php/isi/article/view/124/72
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[17] D. T. Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online,” Creat. Inf. Technol. J., vol. 8, no. 1, pp. 64–77, 2021, doi: 10.24076/citec.2021v8i1.264.
[18] D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” J. Tekno Kompak, vol. 15, no. 1, pp. 131–145, 2021, doi: 10.33365/jtk.v15i1.744.
[19] D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Penerapan Algoritma Svm Untuk Analisis Sentimen Pada Data Twitter Komisi Pemberantasan Korupsi Republik Indonesia,” Edutic - Sci. J. Informatics Educ., vol. 7, no. 1, pp. 1–11, 2020, doi: 10.21107/edutic.v7i1.8779.
[20] R. Sanusi, F. D. Astuti, and I. Y. Buryadi, “Analisis Sentimen Pada Twitter Terhadap Program Kartu Pra Kerja Dengan Recurrent Neural Network,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 2, pp. 89–99, 2021, doi: 10.26798/jiko.v5i2.645.
[21] P. L. Parameswari and Prihandoko, “Penggunaan Convolutional Neural Network Untuk Analisis Sentimen Opini Lingkungan Hidup Kota Depok Di Twitter,” J. Ilm. Teknol. dan Rekayasa, vol. 27, no. 1, pp. 29–42, 2022, doi: 10.35760/tr.2022.v27i1.4671.
[22] M. Z. Rahman, Y. A. Sari, and N. Yudistira, “Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5120–5127, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
Published
2023-03-24
How to Cite
Afrinanda, R., Efrizoni, L., Agustin, W., & Rahmiati, R. (2023). Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 309-324. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2640
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