%0 Thesis
%9 Skripsi
%A Titi Prihartati, NIM.: 22106010021
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2026
%F digilib:75477
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Data Deret Waktu, Prediksi Harga Saham, Deep Learning, RNN, LSTM
%P 133
%T PERBANDINGAN MODEL DEEP LEARNING RECURRENT NEURAL NETWORK DAN LONG SHORT-TERM MEMORY DALAM PREDIKSI HARGA SAHAM (STUDI KASUS: PT TELEKOMUNIKASI INDONESIA TBK MENGGUNAKAN DATA HISTORIS TAHUN 2014–2024)
%U https://digilib.uin-suka.ac.id/id/eprint/75477/
%X Time series data are widely used in analysis and forecasting by utilizing  historical patterns to predict future values. Various classical linear models, such as  ARIMA, are commonly applied due to their strong mathematical foundation but are  limited in capturing complex, nonlinear, non-stationary, and volatile patterns, such  as stock price data. Therefore, deep learning approaches, particularly Recurrent  Neural Network (RNN) and Long Short-Term Memory (LSTM), are used because  they can model nonlinear relationships in sequential data. This study aims to explore  the steps of RNN and LSTM models, implement both models on PT Telkom  Indonesia Tbk stock price data for the period 2014–2024, and compare their performance  in predicting stock prices. Both models were developed with the same architecture,  consisting of two hidden layers, 64 neurons, tanh activation function, and  batch size of 64. Performance evaluation was conducted using MSE, RMSE, and  MAE. The results show that LSTM outperforms RNN with MSE 67.6683, RMSE  55.3865, MAE 41.5585, and training time of 497.18 seconds, whereas RNN has  MSE 9810.32, RMSE 99.05, MAE 79.67, and training time of 195.24 seconds. Although  LSTM requires a longer training time, it is more effective in capturing stock  price movement patterns.
%Z Sri Utami Zuliana, S.Si., M.Sc., Ph.D. dan Deddy Rahmadi, M.Sc.