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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)
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.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
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) (Text)
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) (Other)
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