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Sonia Novel Lase
Universitas Prima Indonesia
Computer Science & IT

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APPLICATION OF DATA MINING TO PREDICATE STOCK PRICE USING LONG SHORT TERM MEMORY METHOD Sonia Novel Lase; Yenny Yenny; Owen Owen; Mardi Turnip; Evta Indra
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Investing some of our wealth to invest in stocks is highly recommended considering the fluctuating nature of stock prices, meaning that stock prices can go up and down at any time depending on the conditions and phenomena that occur on the stock market. Stock investment includes having a high risk of loss but also by taking that risk it is also possible to get high profits (High Risk High Return). Shares are proof of ownership of company value or proof of equity interest. Shareholders are also entitled to receive dividends (profit sharing) according to the number of shares they own. This study aims to make it easier for everyone who wants to invest in Google and Tesla stocks and implement the long short term memory method for stock price prediction. This data mining research resulted in a Root Mean Square Error (RMSE) value of 1.80%, which means the prediction results are very accurate with real data and the average difference between real stock price data and predicted data is $3 -$15.
Co-Authors Evta Indra Mardi Turnip, Mardi Owen Owen Yenny Yenny