Peramalan Harga Saham BBCA Menggunakan Bidirectional Long Short-Term Memory Berdasarkan Data Historis Open, High, Low, dan Close
DOI:
https://doi.org/10.54065/artificial.1993Keywords:
BBCA, Bi-LSTM, Deep Learning, OHLC, Peramalan Harga SahamAbstract
Pergerakan harga saham yang dinamis, berfluktuasi, dan mengalami perubahan tren menjadi tantangan dalam menghasilkan peramalan yang akurat. Penelitian ini bertujuan membangun dan mengevaluasi model Bidirectional Long Short-Term Memory (Bi-LSTM) untuk meramalkan harga saham PT Bank Central Asia Tbk (BBCA.JK) berdasarkan data historis Open, High, Low, dan Close (OHLC). Penelitian menggunakan pendekatan kuantitatif dengan desain pemodelan prediktif berbasis deep learning. Data historis periode Januari 2019 hingga Juni 2026 diperoleh dari Yahoo Finance. Tahapan penelitian meliputi pembersihan data, pengurutan berdasarkan waktu, normalisasi menggunakan Min-Max Scaling, pembentukan sequence melalui teknik sliding window, serta pembagian dataset menjadi 80% data latih, 10% validasi, dan 10% data uji. Model Bi-LSTM dilatih menggunakan optimizer Adam dengan fungsi kerugian Mean Squared Error (MSE), sedangkan kinerjanya dievaluasi menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil evaluasi yang dilaporkan menunjukkan nilai MAPE sebesar 1,82%, yang mengindikasikan tingkat kesalahan relatif rendah pada data pengujian. Hasil prediksi juga menunjukkan kemampuan model mengikuti kecenderungan umum pergerakan harga aktual, sedangkan proyeksi Juli 2026 hingga Juni 2027 memperlihatkan kecenderungan peningkatan harga. Dengan demikian, Bi-LSTM berpotensi digunakan sebagai pendekatan peramalan berbasis pola historis, tetapi hasil proyeksinya perlu diinterpretasikan secara hati-hati dengan mempertimbangkan faktor fundamental, sentimen pasar, dan kondisi ekonomi.
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