@phdthesis{digilib76841,
           month = {April},
           title = {PERBANDINGAN MODEL LONG SHORT-TERM MEMORY (LSTM) DAN GATED RECURRENT UNIT (GRU) DALAM PERAMALAN HARGA SAHAM (STUDI KASUS : INDEKS HARGA SAHAM GABUNGAN (IHSG) PERIODE JANUARI 2010 - DESEMBER 2025)},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22106010047 Irodatul Jannah},
            year = {2026},
            note = {Sri Utami Zuliana, S.Si., M.Sc., Ph.D.},
        keywords = {GRU, LSTM, IHSG, Peramalan Deret Waktu},
             url = {https://digilib.uin-suka.ac.id/id/eprint/76841/},
        abstract = {This study aims to compare the effectiveness of RNNbased
neural network architectures, namely LSTM and GRU,
in forecasting the weekly closing prices of the IHSG from
January 2010 to December 2025. Four model architectures
were employed, namely LSTM?LSTM, GRU?GRU, LSTM?
GRU, and GRU?LSTM. The research stages included data
normalization, sliding window transformation, and splitting
the data into training and testing sets. All models were
trained using the Adam optimizer with the same hyperparameter
configuration. Model performance was evaluated
using MSE and MAPE. The results showed that the GRU?
GRU architecture achieved the best performance with the
lowest MSE and MAPE values, indicating that it was more
effective in capturing the time series patterns of IHSG closing
prices.}
}