Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

Widi Hastomo, Adhitio Satyo Bayangkari Karno, Nawang Kalbuana, Ervina Nisfiani, Lussiana ETP

Abstract


Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

Keywords


Covid-19; Deep Learning; LSTM; GRU

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DOI: http://dx.doi.org/10.26418/jp.v7i2.47411

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