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bibo:abstract "This study aims to compare the effectiveness of RNNbased\r\nneural network architectures, namely LSTM and GRU,\r\nin forecasting the weekly closing prices of the IHSG from\r\nJanuary 2010 to December 2025. Four model architectures\r\nwere employed, namely LSTM–LSTM, GRU–GRU, LSTM–\r\nGRU, and GRU–LSTM. The research stages included data\r\nnormalization, sliding window transformation, and splitting\r\nthe data into training and testing sets. All models were\r\ntrained using the Adam optimizer with the same hyperparameter\r\nconfiguration. Model performance was evaluated\r\nusing MSE and MAPE. The results showed that the GRU–\r\nGRU architecture achieved the best performance with the\r\nlowest MSE and MAPE values, indicating that it was more\r\neffective in capturing the time series patterns of IHSG closing\r\nprices."^^xsd:string;
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