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All Journal International Journal of Electrical and Computer Engineering Coding: Jurnal Komputer dan Aplikasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Building of Informatics, Technology and Science Jurnal Teknologi dan Sistem Tertanam Journal of Telecommunication, Electronics and Control Engineering (JTECE) JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi The Indonesian Journal of Computer Science SISFOTENIKA
Kartika Sari
Universitas Tanjungpura
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Building of Informatics, Technology and Science

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Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine Kasliono, Kasliono; Candraningrum, Niken; Sari, Kartika
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3567

Abstract

The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions
Co-Authors AA Sudharmawan, AA Achyar, Athif Tafrihan Aji, Maulana Setia Alfikri, Nadya Syifa Andy Wijaya Anggraini, Yessa Tria Astaman, Andi Ainur Ridha Azhar, Sahari Bayti Widya Rezky Cucu Suhery Elegant, Gustavo Dipo Fitriandini, Laila Herry Prayuda Hirzen Hasfani Ikhwan Ruslianto Immanuel, Immanuel Indriana, Fitrialfi Irma Nirmala Kasliono Kasliono Marettania, Felisitas Muhammad Syamsul Bahri Muharam Rizqi Ananda Niken Candraningrum Rahmawan, Arif Rahmi Hidayati Randily, Dhea Ramadhanti Putri Salsabila, Bella Sari, Fioni Puspa Siti Oktavia Wulandari Suhardi Suhardi Tedy Rismawan Uray Ristian