Trend Analysis of The Effect of LQ45 Stocks on Stock Price Index Fluctuations using the C4.5 Algorithm with Correlation-Based Feature Selection and Information Gain

Abstract
The research was conducted to reveal the effect of LQ45 stock on the accuracy of stock price index fluctuations using the C4.5 algorithm with Correlation-Based Feature Selection (CFS) and Information Gain (IG) techniques. This study used the superior C4.5 algorithm using a combination feature selection technique between Correlation-based Feature Selection (CFS) and Information Gain in the hope of getting accurate results. Analysis conducted on the LQ45 index through various stages that include data collection, manual pre-processing, validation methods, process features, decision tree model result, and classification accuracy performance. The result of test revealed that the implementation of the C4.5 algorithm using correlation-based feature selection (CFS) and information gain techniques can be applied well to LQ45 stocks. The accuracy generated from the original data (without the selection feature) was 77.857%, while the addition of features to the combination of Correlation-Based Feature Selection (CFS) and Information Gain had a large influence on the results of increasing data accuracy from the accuracy of the original data by 77.857% to 78.333%. Thus, the C4.5 calculation process with the Correlation-based Feature Selection (CFS) feature selection technique alone cannot improve the accuracy level, while when combined with the Information Gain technique, the accuracy processing results will be better (higher).
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