UJI PERFORMA PREDIKSI METODE AUTO REGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGES DAN LONG SHORT-TERM MEMORY DENGAN DATA SAHAM DUA PERUSAHAAN BANK - Berli Suharmanto (2023)
Stocks are one way for people to invest for the long term. Stock data can be used for analysis in a study using an algorithm to obtain predictive value information that can be used in the future. In this study the algorithm used to obtain the prediction model is the Auto Regressive Fractionally Integrated Moving Averages (ARFIMA) and Long Short-Term Memory (LSTM) methods. The stock data used is stock data from IDX with Bank Raya Indonesia Tbk stock data. and Bank IBK Indonesia Tbk. in the period September 2019 to December 2022 with Closing data as a feature. The processing results of the best ARFIMA model are the ARFIMA(8,0.5,0) model from BRI data with SMAPE of 5.57% and the ARFIMA model(4,0.5,0) from Bank IBK Indonesia data with SMAPE of 23.31%. For the best results, BRI's LSTM is 1.61% with 150 epochs and Bank IBK Indonesia's LSTM is 2.22% with 300 epochs. The best model produced is the LSTM model with 150 epochs for BRI and the LSTM model with 300 epochs.