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      "title": "PREDIKSI HARGA SAHAM PT GUDANG GARAM TBK\r\nMENGGUNAKAN VARIASI ARSITEKTUR LSTM-GRU\r\nDENGAN OPTIMASI HYPERPARAMETER",
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      "abstract": "Pasar saham merupakan instrumen investasi dengan tingkat volatilitas\r\ntinggi sehingga pergerakan harga sulit diprediksi secara akurat, khususnya pada\r\nsaham PT Gudang Garam Tbk (GGRM) yang mengalami fluktuasi signifikan dan\r\npenurunan kinerja dalam beberapa tahun terakhir. Kondisi ini menimbulkan\r\nketidakpastian dalam pengambilan keputusan investasi, sehingga diperlukan\r\nmetode prediksi yang mampu menangkap pola kompleks pada data deret waktu.\r\nPenelitian ini bertujuan untuk menganalisis peningkatan performa kinerja model\r\nLong Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) dengan\r\npendekatan model Hybrid LSTM–GRU dengan optimasi Hyperparameter\r\nmenggunakan metode Grid Search dalam memprediksi harga saham GGRM.\r\nTahapan penelitian meliputi pengumpulan data historis periode 2015–2025,\r\nPreprocessing data (pembersihan data, penambahan indikator teknikal,\r\nnormalisasi, dan pembentukan sliding window), pembangunan dan pelatihan\r\nmodel, serta evaluasi menggunakan metrik MAE, RMSE, MAPE dan koefisien\r\ndeterminasi (R2). Hasil penelitian menunjukkan bahwa model Hybrid LSTM–GRU\r\nyang dioptimasi menggunakan Grid Search mampu memberikan performa prediksi\r\nyang baik. Penelitian ini diharapkan dapat memberikan kontribusi dalam\r\npengembangan model prediksi harga saham berbasis deep learning, khususnya\r\ndalam meningkatkan performa model melalui pendekatan Hybrid dan optimasi\r\nHyperparameter, serta sebagai alat bantu dalam pengambilan keputusan investasi.",
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            "lineage": null,
            "family": "Sasongko",
            "given": "Theopilus Bayu"
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