Visualisasi Prediksi Harga Saham Menggunakan Model Long Short-Term Memory (LSTM) - Windi Irmayani; Wanty Eka Jayanti (Jurnal Sistem Informasi Akuntansi, 2025)
The stock market is a dynamic investment instrument affected by various factors, making it difficult to analyze. Traditional methods such as technical and fundamental analysis are often less accurate in capturing non-linear patterns and complex historical data. Deep learning algorithms, especially Long Short-Term Memory (LSTM), are beginning to be used to improve the accuracy of stock price predictions due to their ability to handle time series data and retain long-term information. This study focuses on the stock price prediction of Apple Inc., a leading technology company that reflects global industry trends. LSTM is used to overcome the limitations of traditional methods and provide a more accurate prediction model, especially in capturing stock price fluctuations due to new product launches, financial reports, and regulatory changes.This study aims to contribute to academics and market players in making more accurate prediction-based investment decisions, while expanding insights into the application of deep learning in the stock market. The result, it appears that the overall data trend is moving upwards, although there are some local (minor) declines.