Multivariate Approach of LSTM Model for Forecasting Tjiwi Kimia Tbk Stock Price

Muhammad Zaki Naufal; Sulistyo Dwi Sancoko

doi:10.36378/jtos.v9i2.6068

Muhammad Zaki Naufal University of Technology Yogyakarta
Sulistyo Dwi Sancoko University of Technology Yogyakarta

DOI: https://doi.org/10.36378/jtos.v9i2.6068

Keywords: Prediction, Stocks, Price, Long Short-Term Memory, Multivariate

Abstract

Stocks are a type of long-term financial instrument found in the capital market. However, stock price movements are volatile and influenced by various factors such as economic conditions, market sentiment, government policies, and global dynamics requiring market participants or investors to conduct analysis before entering the market. Analysis generally falls into two categories: fundamental and technical; yet, even when employing these techniques, investors may still face the risk of failure. To address this issue, this study designs and develops a system to predict Indonesian stock closing prices using the Long Short-Term Memory (LSTM) model, aiming to provide investors with insights for their investment planning. The study utilizes OHLC (Open, High, Low, Close) data for the target stocks and the closing price of the Jakarta Composite Index (JCI) as supplementary data. Evaluation is conducted using RMSE and MAE metrics. The results demonstrate that the prediction system is capable of forecasting closing prices with a Root Mean Square Error (RMSE) of 280 and a Mean Absolute Error (MAE) of 210.55. This model is expected to assist stock market participants and investors in making investment decisions.

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