OPTIMALISASI PREDIKSI SAHAM APPLE DAN SAMSUNG DENGAN ALGORITMA BPNN

Atika Mutiarachim; Yupie Kusumawati; Nurchayati Nurchayati; Aulia Indriawati

doi:10.46576/djtechno.v6i2.6707

OPTIMALISASI PREDIKSI SAHAM APPLE DAN SAMSUNG DENGAN ALGORITMA BPNN

Atika Mutiarachim, Yupie Kusumawati, Nurchayati Nurchayati, Aulia Indriawati

Abstract

Pasar saham global khususnya bidang teknologi mengalami volatilitas yang signifikan, dengan saham Apple Inc. dan Samsung Electronics Co., Ltd sebagai pemain utama. Prediksi signifikan sangat diperlukan untuk mengurangi resiko investasi. Penelitian ini menganalisis dan membandingkan kinerja Backpropagation Neural Network (BPNN) dalam memprediksi pergerakan saham Apple dan Samsung. Dataset publik diperoleh dari Kaggle, saham Samsung dengan 6128 data periode 4 Januari 2000 sampai 13 Juni 2024 dan saham Apple dengan 2476 data periode 2 Januari 2014 sampai 31 Oktober 2023. Metode BPNN diterapkan dengan optimasi parameter learning rate, momentum, dan training cycle, pembagian data 10-fold cross validation, evaluasi nilai Root Mean Square Error (RMSE). Hasil terbaik menunjukkan konfiguasi optimal diperoleh dari learning rate 0.1, momentum 0.9, error epsilon 1.0E-4 dan training cycle 60. Nilai RMSE terbaik saham Apple 0.802 0.263 dengan akurasi 99.85%, dan pada saham Samsung RMSE terbaik 399.806 102.670 dengan akurasi 99.36%. Penelitian membuktikan BPNN dengan pola 0.1-0.9-60 sangat efektif memprediksi harga Close sehingga mampu memberikan kontribusi signifikan bagi investor dalam melakukan evaluasi investasi sebagai strategi meminimalisir resiko saham.

Keywords

Backpropagation Neural Network; Stock Price Prediction; Apple; Samsung; Machine Learning

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DOI: https://doi.org/10.46576/djtechno.v6i2.6707