Prediksi Saham Volatil di Indonesia menggunakan Metode CNN-LSTM

Abstract

This research aims to predict the prices of four volatile stocks using the CNN-LSTM method using several stocks as research data. These shares include shares of SCMA.JK (PT Surya Citra Media Tbk) which is a company operating in the mass media sector, INDY.JK (PT. Indika Energy Tbk) which is an integrated company covering energy resources, energy services, and the energy infrastructure business, especially in the coal segment, ANTM.JK (PT Aneka Tambang Tbk) which is the largest nickel producing mining company in Indonesia, and LPPF.JK (PT Matahari Department Store Tbk) which is the largest retail platform in Indonesia. The results of this research show that CNN-LSTM method has a better R-Squared, MAE, dan RMSE score compared to the LSTM method. Thus, it can be concluded that the CNN-LSTM method is a superior method compared to the LSTM method in predicting volatile stock prices in Indonesia.