Prediksi Harga Saham Bank Central Asia Menggunakan Algoritma Deep Learning GRU - Prayogi, Kurnia; Gata, Windu; Kussanti, Devy Putri (STMIK Banjarbaru, 2024)
Stocks are an essential part of investment, often known for their fast-paced price fluctuations. Investing in stocks also requires strategies for deciding and predicting future stock prices, with current methods including technical, sentiment, and fundamental analysis. The aim of this research is to predict stock prices for PT Bank Central Asia's stock data from 2019 to 2024 using a deep learning classification algorithm, namely Gated Recurrent Units (GRU). The implementation of the model here is to find values such as RMSE, MSE, MAE, R-Squared, MGD, and MPD, and for evaluation metrics, values such as accuracy, f1-score, precision, and recall are sought. The dataset is divided into two models: training data and test data, with a model split of 80:20 and 60:40. The research results also indicate that the use of the 80:20 model appears to be better than the 60:20 model with a lookback of 15, timestep of 15, and epoch of 50, which yields RMSE 1.039, MSE 1.079, MAE 0.842, R-Squared 0.983, MGD 0.0037, and MPD 0.0197, along with an accuracy result of 54.87%, recall of 59.23%, f1-score of 58.11%, and precision of 57.03%.Keywords: Stocks; Bank Central Asia; Deep Learning; Gated Recurrent UnitsÂ AbstrakSaham adalah suatu bagian penting dalam investasi yang sering dikenal dengan investasi dengan fluktuasi harga yang cenderung cepat. Dalam berinvestasi saham juga membutuhkan strategi dalam memutuskan dan memprediksi harga saham kedepannya dimana untuk saat ini metode yang masih digunakan berupa analisis teknis, sentiment, dan fundamental. Penelitian saat ini bertujuan untuk melakukan prediksi harga saham terhadap data saham PT Bank Central Asia dari tahun 2019 sampai 2024 menggunakan algoritma klasifikasi deep learning, yaitu Gated Recurrent Units (GRU). Penerapan model disini untuk mencari nilai RMSE, MSE, MAE, R-Squared, MGD dan MPD lalu untuk nilai evaluasi mencari nilai accuracy, f1-score, precision, dan recall. Dataset yang dibagi menjadi dua model yaitu data latih dan data uji dengan model 80:20 dan 60:40, hasil penelitian juga memperlihatkan penggunaan model 80:20 terlihat lebih baik daripada model 60:20 dengan lookback 15, timestep 15, dan epoch 50, yang memiliki nilai RMSE 1.039, MSE 1.079, MAE 0.842, R-Squared 0.983, MGD 0.0037 dan MPD 0.0197 lalu hasil accuracy sebesar 54.87%, recall 59.23%, f1-square 58.11%, precission 57.03%.Â