Optimization of stock price prediction of PT Aneka Tambang Tbk (ANTM) using Long Short-Term Memory

Ilham Maulana; Mellisa Mellisa; Jumanto Jumanto

doi:10.31315/telematika.v22i1.10921

Authors

Ilham Maulana Universitas Negeri Semarang
Mellisa Mellisa Universitas Negeri Semarang
Jumanto Jumanto Universitas Negeri Semarang

DOI:

https://doi.org/10.31315/telematika.v22i1.10921

Keywords:

LSTM, ANTM, Stock, RMSE, Stock price prediction,

Abstract

Purpose: Develop a machine learning model to predict stock market activity by finding the Root Mean Squared Error (RMSE) value.

Design/methodology/approach: LSTM (Long Short-Term Memory) is one of the machine learning techniques that can be used to anticipate traffic in realtime. Using this method can be used to analyze stock market activity that has time series data.

Findings/result: This research obtained a Root Mean Squared Error (RMSE) value of 43.32.

Originality/value/state of the art: By using the same machine learning method as the previous research, namely LSTM. The research provides more efficient results.

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