Analyze News Effect on Trend Stock Price in Indonesia Based on Bidirectional-Long Short Term Memory

Abstract

News platforms such as BBC, UN News, and CNN are news sites that are very global, both globally and nationally. With this site, someone can find information in other countries or their country. The news contained on the BBC website can be analyzed using sentiment analysis. Sentiment analysis is carried out to see whether the news tends to be positive, negative, or neutral so that researchers or institutions can find out how the response of the news is to other sectors such as stocks in Indonesia. With the IDX website as a list of company shares in Indonesia, sentiment analysis can be carried out on news on the BBC website that can affect the rise or fall of stock prices in Indonesia using a combination of Word2Vec and the Bidirectional- Long Short Term Memory (BiLSTM) method. The BiLSTM method is an algorithm that has a function to process text data to predict the value of stock price trends by utilizing Word2Vec for word embedding of news. In this study, the dataset used is international news on the BBC website and historical stock prices of several companies on the IDX website. This study utilizes both methods to be able to predict stock price trends. By using 15.674 data, this study shows that the BiLSTM method has an average accuracy rate of 80.03%.