Prediksi Indeks BEI dengan EnsembleConvolutional Neural Networkdan Long Short-Term Memory

Prediksi Indeks BEI dengan EnsembleConvolutional Neural Networkdan Long Short-Term Memory

BEIindexes, ensemble, Convolutional Neural Network, Long Short-Term Memory, deep learning, time series data

he Indonesian Stock Exchange (IDX) stock market index isone of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. Thisstudy proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Resultsof experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM

Harya Widiputra1, Adele Mailangkay2, Elliana Gautama

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/23

Perbanas Institute

20 juni 2021

Fajar bagus W

PDF

Indonesia

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