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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Lontar Komputer: Jurnal Ilmiah Teknologi Informasi ILMU KELAUTAN: Indonesian Journal of Marine Sciences Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) JURNAL SUMBER DAYA AIR Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering
Didit Adytia
Telkom University
Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

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Forecasting of GPU Prices Using Transformer Method Risyad Faisal Hadi; Siti Sa'adah; Didit Adytia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1569

Abstract

GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code. The price of GPU/VGA has fluctuated significantly since the start of the COVID-19 pandemic in 2020, due in part to the increased demand for GPUs for remote work and online activities. Furthermore, accurate GPU price forecasting can have broader implications beyond the computer hardware industry, with potential applications in investment decision-making, production planning, and pricing strategies for manufacturers. This research aims to forecast future GPU prices using deep learning-based time series forecasting using the Transformer model. We use daily prices of NVIDIA RTX 3090 Founder Edition as a test case. We use historical GPU prices to forecast 8, 16, and 30 days. Moreover, Transformer we compare the results of the Transformer model with two other models, RNN and LSTM. We found that to forecast 30 days; the Transformer model gets a higher coefficient of correlation (CC) of 0.8743, a lower root mean squared error (RMSE) value of 34.68, and a lower mean absolute percentage error (MAPE) of 0.82 compared to the RNN and LSTM model. These results suggest that the model is an effective and efficient method for predicting GPU prices.
Co-Authors - Adiwijaya Adiwijaya Alif Rizal Yonanta Alifa Puspa Yuninda Andonowati Andonowati Aniq Atiqi Rohmawati Annas Wahyu Ramadhan Arionmaro Asi Simaremare Arkan Priya Anggana Hadna Arnida Lailatul Latifah Azka Khoirunnisa Cornelius Stephanus Alfredo Dede Tarwidi Deni Saepudin Derry Destian E. van Groesen Faishal Rasyid Fhira Nhita Four Saputra BM Hanif Fakhrurroja Huda Bachtiar I Putu Samskerta Indira Citra Widya Indra A Aditya Isman Kurniawan kemas muslim Kemas Muslim Lhaksmana, Kemas Muslim Leo Sembiring Lukman Hardian Muhammad Adzhar Amrullah Muhammad Rafiuddin Nopendri Nopendri Novalianda Jeriano Nugrahinggil Subasita Putu Harry Gunawan R. Fajrika hadnis Putra Radika Rafif Gibran Rasyadan Farouq Riam Badriana Risyad Faisal Hadi Semeidi Husrin Semeidi Husrin Seno Adi Putra Setyorini Setyorini Siti Sa'adah Tatang Mulyana Tien Fabrianti Kusumasari Widianto Soekarnen