Prediksi Harga Saham Yang Bersifat Siklikal Di Indonesia Menggunakan Metode LSTM dan SVM - Gabriel Adisurya Harsono; Alexander Setiawan; Hans Juwiantho (Universitas Kristen Petra, 2022)
Stock Market prediction is not an easy task, eventhou using technical analysis. Technical analysis is a technique that can predict stock market price. But using this technique is not an easy task, hence there is a change of mistake in analyzing the stoks that create losses for investors and traders. This is where machine learning and deep learning are able to predict a stock that have cyclical behavior. Cyclical stocks have patterns that other stocks do not have, where machine learning and deep learning model are able to catch and learn the pattern on cyclical stocks and predict the stock prices. The prediction are used to give recommandation for the users when to but or when to sell stocks, but it also able to give recommandation and show stock prices a year in the future. In this Undergraduatae Thesis will be used five methode and two type of data, this include LSTM, Bi-LSTM, GRU, SARIMAX, and SVR, the data contains multivariate and univariate type of data. Multivariate data will contain the stock prices, Jakarta composite index, and the support data close prices. The result of prediction will be compared with the existing data using RMSE then compare the result with all existing model. In the proses of the undergraduate thesis, every parameters in the model will be searched using GridsearchCV and the best parameter for each models. From this research found 1 best metode for multivariate prediction and one for univariate. for multivariate the best parameter for the prediction is LSTM where the RMSE value is 63.67 training and 74.82 for testing. For univariate prediction the best metode is SVR where the RMSE value is 58.04 for training and 75.29 for testing.