Improving Algorithm Performance using Feature Extraction for Ethereum Forecasting

Indri Tri Julianto; Dede  Kurniadi; Ricky Rohmanto; Fathia Alisha Fauzia

doi:10.29207/resti.v8i1.4872

Indri Tri Julianto Institut Teknologi Garut
Dede Kurniadi Institut Teknologi Garut
Ricky Rohmanto Ma'soem University
Fathia Alisha Fauzia Ma'soem University

DOI: https://doi.org/10.29207/resti.v8i1.4872

Keywords: Algorithms, Ethereum, Feature Extraction, Forecasting

Abstract

Ethereum is a cryptocurrency that is now the second most popular digital asset after Bitcoin. High trading volume is the trigger for the popularity of this cryptocurrency. In addition, Ethereum is home to various decentralized applications and acts as a link for Decentralized Finance (DeFi) transactions, Non-Fungible Tokens (NFTs) and the use of smart contracts in the crypto space. This study aims to improve the performance of the forecasting algorithm by using feature extraction for Ethereum price forecasting. The algorithms used are neural networks, deep learning, and support vector machines. The research methodology used is Knowledge Discovery in Databases. The data set used comes from the yahoo.finance.com website regarding Ethereum prices. The results show that the neural network Algorithm is the best Algorithm compared to Deep Learning and support vector machine. The root mean square error value for the neural network before feature selection is 93,248 +/- 168,135 (micro average: 186,580 +/- 0,000) Linear Sampling method and 54,451 +/- 26,771 (micro average: 60,318 +/- 0,000) Shuffled Sampling method. Then after feature selection, the root mean square error value improved to 38,102 +/- 31,093 (micro average: 48,600 +/- 0,000) using the Shuffled Sampling method

Downloads

Download data is not yet available.

References

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. Trans. A Basics, vol. 34, no. 1, pp. 140–148, 2021, doi: 10.5829/IJE.2021.34.01A.16.

I. T. Julianto, D. Kurniadi, F. A. Fauziah, and R. Rohmanto, “Improvement of Data Mining Models using Forward Selection and Backward Elimination with Cryptocurrency Datasets,” J. Appl. Intell. Syst., vol. 8, no. 1, pp. 100–109, 2023.

R. Setiawan, I. T. Julianto, and F. F. Roji, “Time Series Forecasting of Top 3 Ranking Cryptocurrencies,” J. Appl. Intell. Syst., vol. 8, no. 2, pp. 193–205, 2023.

N. A. Hitam, A. R. Ismail, and F. Saeed, “An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for Cryptocurrency Forecasting,” Procedia Comput. Sci., vol. 163, pp. 427–433, 2019, doi: 10.1016/j.procs.2019.12.125.

D. L. Kuo Chuen, L. Guo, and Y. Wang, “Cryptocurrency: A New Investment Opportunity?,” SSRN Electron. J., vol. 20, no. 3, pp. 16–40, 2018, doi: 10.2139/ssrn.2994097.

A. Prasetya, F. Ferdiansyah, Y. N. Kunang, E. S. Negara, and 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, doi: 10.33557/journalisi.v3i2.124.

N. Alnuaimi, A. Almemari, M. Madine, K. Salah, H. Al Breiki, and R. Jayaraman, “NFT Certificates and Proof of Delivery for Fine Jewelry and Gemstones,” IEEE Access, vol. 10, pp. 101263–101275, 2022, doi: 10.1109/ACCESS.2022.3208698.

S. Paavolainen and C. Carr, “Security Properties of Light Clients on the Ethereum Blockchain,” IEEE Access, vol. 8, pp. 124339–124358, 2020, doi: 10.1109/ACCESS.2020.3006113.

S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),” J. Media Inform. Budidarma, vol. 5, no. 2, pp. 406–414, 2021, doi: 10.30865/mib.v5i2.2835.

Y. Religia, “Feature Extraxtion Untuk Klasifikasi Pengenalan Wajah Menggunakan Support Vector Machine dan K-Nearest Neighbour,” Pelita Teknol. J. Ilm. Inform. Arsit. dan Lingkung., vol. 14, no. 2, pp. 85–92, 2019.

A. Priyambodo and Prihatini, “Evaluasi Ekstraksi Fitur Klasifikasi Teks Untuk Peningkatan Akurasi Klasifikasi Menggunakan Naive Bayes,” J. Ilm. Elektron. dan Komput., vol. 13, no. 1, pp. 159–175, 2020.

M. Muhathir, M. H. Santoso, and D. A. Larasati, “Wayang Image Classification Using SVM Method and GLCM Feature Extraction,” J. Informatics Telecommun. Eng., vol. 4, no. 2, pp. 373–382, 2021, doi: 10.31289/jite.v4i2.4524.

K. Ayuningsih, Y. A. Sari, and P. P. Adikara, “Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 3, no. 4, pp. 3166–3173, 2019.

M. Saenudin, F. Haq, R. I. Adam, I. Engineering, and S. Program, “Classification of Covid-19 Using Feature Extraction GLCM and SVM Algorithm,” J. Mantik, vol. 5, no. 1, pp. 179–183, 2021.

I. T. Julianto, “Design And Build Virtual Reality Photography Web-Based To Support Tourism,” J. Electr. Electron. Information, Commun. Technol., vol. 3, no. 2, p. 58, Oct. 2021, doi: 10.20961/jeeict.3.2.54833.

Yahoo Finance, “Ethereum USD (ETH-USD),” yahoo.finance.com, 2022. https://finance.yahoo.com/quote/ETH-USD/history?period1=1438905600&period2=1669680000&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true.

A. D. Savitri, F. A. Bachtiar, and N. Y. Setiawan, “Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus : Belle Crown Malang),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 9, pp. 2957–2966, 2018.

Mikhael, F. Andreas, and U. Enri, “Perbandingan Algoritma Linear Regression, Neural Network, Deep Learning, Dan K-Nearest Neighbor (K-Nn) Untuk Prediksi Harga Bitcoin,” JSI J. Sist. Inf., vol. 14, no. 1, pp. 2450–2464, 2022, [Online]. Available: http://ejournal.unsri.ac.id/index.php/jsi/index.

I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, and A. Mulyani, “Data Mining Algorithm Testing For SAND Metaverse Forecasting,” J. Appl. Intell. Syst., vol. 7, no. 3, pp. 259–267, 2022.

D. Hediyati and I. M. Suartana, “Penerapan Principal Component Analysis (PCA) Untuk Reduksi Dimensi Pada Proses Clustering Data Produksi Pertanian Di Kabupaten Bojonegoro,” J. Inf. Eng. Educ. Technol., vol. 5, no. 2, pp. 49–54, 2021, doi: 10.26740/jieet.v5n2.p49-54.

A. P. Putra, N. W. Wiantari, P. M. Novita Dewi, and I. D. M. Bayu Atmaja Darmawan, “Independent Component Analysis (ICA) Dan Sparse Component Analysis (SCA) Dalam Pemisahan Vokal Dan Instrumen Pada Seni Geguntangan,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 8, no. 1, p. 105, 2019, doi: 10.24843/jlk.2019.v08.i01.p13.

F. H. Hamdanah and D. Fitrianah, “Analisis Performansi Algoritma Linear Regression dengan Generalized Linear Model untuk Prediksi Penjualan pada Usaha Mikra, Kecil, dan Menengah,” J. Nas. Pendidik. Tek. Inform., vol. 10, no. 1, p. 23, 2021, doi: 10.23887/janapati.v10i1.31035.

M. F. Chania, O. Sara, and I. Sadalia, “Analisis Risk dan Return Investasi pada Ethereum dan Saham LQ45,” Stud. Ilmu Manaj. dan Organ., vol. 2, no. 2, pp. 139–150, 2021, doi: 10.35912/simo.v2i2.669.

T. Sellar and A. A. Arulrajah, “The Role of Social Support on Job Burnout in the Apparel Firm,” Int. Bus. Res., vol. 12, no. 1, p. 110, 2018, doi: 10.5539/ibr.v12n1p110.