Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)

Rizkilloh, Moch Farryz; Widiyanesti, Sri

doi:10.29207/resti.v6i1.3630

Cited by 15 publications

(14 citation statements)

References 7 publications

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“…Xmin is the minimum value of all data, and Xmax denotes the maximum value out of the whole dataset. In addition, the Min-max scaler is enabled to minimise errors that occur during data modelling [21].…”

Section: Pre-processingmentioning

confidence: 99%

Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

Sari¹,

Kusrini²,

Hidayat³

et al. 2023

Indonesian J. Comput. Cybern. Syst.

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As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.

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Section: Pre-processingmentioning

confidence: 99%

Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

Sari¹,

Kusrini²,

Hidayat³

et al. 2023

Indonesian J. Comput. Cybern. Syst.

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show abstract

“…This process is a model test by forecasting the remaining ISPU data that is not included in the training process by 20%. This process produces data from comparisons between predicted and actual data (21). The results of the testing process are used in the next stage, namely model evaluation.…”

Section: Testing Processmentioning

confidence: 99%

Makassar City Air Pollution Forecast in 2045

Jusri,

Jamil,

Barkey

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Air pollution is a problem for all countries in the world, especially for urban areas, because most sources of air pollution are in urban areas. In 2019, it was predicted that ambient air pollution from PM2.5 caused 4.2 million premature deaths globally. It contributes to cancer, respiratory, and cardiovascular problems. Makassar is the most populous city in South Sulawesi Province, with a population of 1,427,619 in 2021. The number of motorized vehicles in 2021 is 1,740,793 units and 1,113 industries. With these conditions, Makassar City has a high potential for air pollution. This research aims to forecast the air pollution level of Makassar City in 2045. The forecasting results can be a reference in developing an air pollution control plan in Makassar City. The forecasting model used is LSTM which is the development of RNN architecture. The results of air pollution forecasting are displayed in the form of an air pollution standard index (ISPU) for easy understanding by the general public. Based on the forecasting results in 2045, the level of air pollution in Makassar City is in the moderate and unhealthy categories. Despite still being dominated by the moderate category, it should be a concern that air quality is no longer in the good category.

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“…Pada penelitian sebelumnya, pendekatan Deep Learning dalam melakukan prediksi telah banyak dilakukan seperti melakukan prediksi harga ponsel dengan Random Forest [6], Prediksi mata uang Bitcoin menggunakan LSTM dan sentiment analisis pada Sosial Media [7], Price movement prediction of cryptocurrencies menggunakan sentiment analysis and Machine Learning [8], Prediksi harga cryptocurrency menggunakan algoritme LSTM [9], Prediksi harga minyak mentah menggunakan Jaringan Syaraf Tiruan (JST) [10], Prediksi belanja pemerintah Indonesia menggunakan Long Short-Term Memory (LSTM) [11], Prediksi penggunaan energi listrik pada rumah hunian menggunakan Long Short-Term Memory [12]. Namun pada penelitian ini, prediksi dilakukan pada komoditas pangan dari hasil pertanian menggunakan algoritme LSTM.…”

Section: Pendahuluanunclassified

Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM)

Adi¹,

Sudianto²

2022

bits

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Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%

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Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)

Cited by 15 publications

References 7 publications

Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

Makassar City Air Pollution Forecast in 2045

Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM)

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