Data Mining Using Linear Regression to Predict the Stock Price of Shipping Companies
DOI:
https://doi.org/10.30649/japk.v10i2.13Keywords:
data mining, linear regression, stock price, CRISP-DM, root mean square errorAbstract
The movement of the closing price of BULL shares tends to experience price variations every day. Investors need appropriate action, so that existing risks can be reduced by knowing the ups and downs of stock prices in the future and predicting the optimal policy steps to make appropriate share buying / selling decisions. The purpose of this study is to apply data mining using linear regression to predict the share price of shipping companies. The research location is on the Indonesia Stock Exchange, Jakarta. The population in this study are all shipping companies listed on the Indonesia Stock Exchange. The type of nonprobability sampling chosen was purposive sampling and quota sampling. The purposive sampling used is 1 shipping company, namely PT. Buana Lintas Lautan, Tbk (BULL). The sampling quota used in this study is the time series data for the daily opening price, the highest price, the lowest price, the closing price, and the volume of shares during the BULL daily period for 1 year 2 months between June 2019 and July 2020. This study uses the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The data mining process based on CRISP-DM consists of 6 phases, namely Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The results showed that there is still a difference between the closing price of the output of the test data and the closing price of the actual shares on the stock exchange. Evaluation of the value of Root Mean Square Error (RMSE) shows the plus number 7.522 from the actual data on the closing price of shares in the daily period of PT. BULL.
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