Penerapan Long Short-Term Memory (LSTM) untuk Peramalan Harga Penutupan Saham BBCA.JK
DOI:
https://doi.org/10.54065/artificial.2010Keywords:
BBCA.JK, Deep Learning, Deret Waktu, LSTM, Peramalan Harga SahamAbstract
Peramalan harga saham merupakan permasalahan yang kompleks karena pergerakan harga bersifat dinamis, fluktuatif, dan memiliki ketergantungan temporal antarperiode. Kondisi tersebut mendorong penggunaan metode deep learning yang mampu mempelajari pola historis pada data deret waktu. Penelitian ini bertujuan mengembangkan dan mengevaluasi model Long Short-Term Memory (LSTM) untuk meramalkan harga penutupan saham PT Bank Central Asia Tbk (BBCA.JK) berdasarkan data historis perdagangan. Data penelitian terdiri atas 1.201 observasi harian pada periode 8 Agustus 2020 hingga 15 Agustus 2025 yang diperoleh dari Kaggle. Dataset mencakup atribut Open, High, Low, Close, dan Volume, sedangkan harga Close digunakan sebagai fitur utama dalam pemodelan. Data dinormalisasi menggunakan Min-Max Scaling dan dibentuk menjadi sekuens menggunakan sliding window 60 hari, kemudian dibagi secara kronologis dengan rasio 80:20 untuk data pelatihan dan pengujian. Model LSTM dibangun menggunakan satu lapisan LSTM dengan 96 unit yang diikuti lapisan Dense serta dilatih menggunakan optimizer Adam dan Huber Loss dengan dukungan Early Stopping, Model Checkpoint, dan ReduceLROnPlateau. Hasil pengujian menghasilkan MAE sebesar 128,57, RMSE sebesar 164,44, R² sebesar 0,9417, dan MAPE sebesar 1,41%. Model juga digunakan untuk menghasilkan estimasi harga selama 30 hari perdagangan berikutnya. Hasil penelitian menunjukkan bahwa LSTM mampu memodelkan pola temporal harga penutupan BBCA.JK dengan tingkat kesalahan relatif rendah pada dataset dan konfigurasi penelitian yang digunakan.
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