Prediksi Harga Penutupan Indeks Harga Gabungan (IHSG) Menggunakan Algoritma Long Short-Term Memory Berbasis Data Historis - Muhammad Farhan Fadhila; Rian Septian Anwar; Albert Riyandi (Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai, 2025)
The dynamic fluctuation of stock prices makes market movement prediction a significant challenge in financial analysis. This study aims to develop and evaluate a Long Short-Term Memory (LSTM) model to predict the daily closing price of the Indonesia Composite Index (IDX Composite) using historical data from 2005 to 2025. The methodology employed includes systematic data pre-processing, such as normalization and the creation of sequential input, as well as the implementation of a two-layer LSTM architecture. Model performance was evaluated through two approaches, namely experimental testing on test datasets and practical validation in short-term prediction scenarios. Experimental results demonstrate very high accuracy, with a Mean Absolute Percentage Error (MAPE) value of 0.69% on the test data. This performance consistency is reinforced by the practical validation, which yielded an overall MAPE of 0.34%, proving the model's capability in predicting previously unseen data. Thus, the hypothesis that the LSTM model can achieve significant prediction accuracy (MAPE<10%) is accepted. Overall, the developed LSTM model is proven to be highly effective and valid, making it a suitable tool to support investment decisions in the stock market.