Implementation Of The ARIMA Method In Predicting LQ 45 Stock Prices (UNTR Issuer)
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5656Abstract
The implementation of technology is used in running businesses or activities that generate profits, such as predicting investments on the stock exchange through transaction data in the transaction data base. Machine learning is an algorithm that produces an approximation function that connects input variables so that it has the potential to be implemented in stock predictions. Stock investment has the characteristics of high risk - high return. Losses are caused by investors' lack of knowledge. Stock value analysis is divided into two, namely fundamental analysis and technical analysis. Technical analysis uses data or records about the market to try to access the demand and supply of a particular stock or the market as a whole. Based on the problems found by investors or bankers, this research will use the autoregressive integrated moving average (ARIMA) method to predict stock price movements. The Arima method consists of four stages, namely identifying time series methods, estimating parameters for alternative methods, testing methods and estimating time series values. Based on these problems, the ARIMA method will be used to predict stock movements. The Arima model (1,0,2) with RMS: 2200.576849857124 successfully predicted for the next 180 days
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