A STOCK PREDICTION SYSTEM USING TEKNIKAL INDICATORS WITH THE LSTM METHOD

Abstract

The capital market industry in Indonesia is developing in a better direction so that the growth of new investors is also increasing. Until the end of February 2021, operational data from the Indonesian Stock Exchange (IDX) and data from the Indonesian Central Securities Depository (KSEI) recorded that the number of new capital market investors had increased by 16.35% or 634,350 investors, from the previous 3,880,753 investors. to 4,515,103 investors. The development of the capital market industry in Indonesia, which has increased investor interest in investing, is expected to mobilize public funds to support national economic development. Some companies that are familiar to the community are BCA, BNI, BRI and MANDIRI. This study attempts to forecast banking stock prices on the LQ45 index, using the Long Short-Term Memory (LSTM) method. LSTM is one of the Recurrent Neural Networks (RNN) which has good accuracy in predictions. The identified fields are Close, Open, RSI, MACD and MA. The evaluation method used in this prediction system is MAPE in the form of percent output.