Prediksi Harga Minyak Dunia Dengan Metode Deep Learning

Fountain of Informatics Journal

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Title Prediksi Harga Minyak Dunia Dengan Metode Deep Learning
 
Creator Hussein, Muhammad; Universitas Muhammadiyah Malang
Azhar, Yufis; Universitas Muhammadiyah Malang
 
Subject LSTM, deep learning, peramalan, harga minyak
 
Description AbstrakPeramalan seri waktu mendapatkan banyak perhatian dari berbagai penelitian. Salah satu data seri waktu yang barubah setiap periode tertentu adalah minyak bumi. Secara umum harga minyak bumi dipengarui oleh dua hal yaitu permintaan dan pendapatan. Pada penelitian ini menggunakan state-of-the-art model Deep Learning LSTM (Long Short Term Memory) untuk meramalkan harga minyak dalam periode tertentu. Metode ini digunakan karena arsitekturnya dapat beradaptasi dengan belajar non-linear dari data seri waktu yang kompleks. Dataset yang digunakan adalah data Brent Oil Price yang selalu di update setiap minggu. Dataset ini berisi harga minyak brent dari tahun 1987 sampai sekarang. Beberapa model yang dibangun terbukti dapat meramalkan harga minyak dengan baik. Model terbaik yang didapatkan dari penelitian ini memiliki RMSE 0,0186 dan MAE 0,013.Kata kunci: LSTM, deep learning, peramalan, harga minyak Abstract[Forecasting World Oil Price with Deep Learning Method] Time series forecasting gets a lot of attention from various studies. One of the time-series data that changes every certain period is petroleum. In general, the price of petroleum is affected by two things, namely demand and income. This research uses a state-of-the-art Deep Learning LSTM (Long Short-Term Memory) model to predict the oil price in a certain period. This method is used because the architecture can adapt to non-linear learning from complex time series data. The dataset used is the Brent Oil Price data, which is always updated every week. This dataset contains the price of Brent oil from 1987 to the present. The models that were built proved to be able to predict oil prices well. The best models obtained from this study have RMSE 0.0186 and MAE 0.013.Keywords: LSTM, deep learning, forecasting, oil price
 
Publisher Universitas Darussalam Gontor
 
Contributor
 
Date 2021-01-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/4446
10.21111/fij.v6i1.4446
 
Source Fountain of Informatics Journal; Vol 6, No 1 (2021): Mei; 29-34
2548-5113
2541-4313
 
Language eng
 
Relation https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/4446/pdf_60
 
Rights Copyright (c) 2021 Fountain of Informatics Journal
http://creativecommons.org/licenses/by-nc-sa/4.0
 

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