JAKARTA COMPOSITE INDEX (JCI) PREDICTION USING K-NEAREST NEIGHBORS REGRESSION
DOI:
https://doi.org/10.64617/kreyat.v2i1.15-23Keywords:
k-NN, regresi, prediksi, Machine-Learning, JCIAbstract
This paper aims to evaluate the accuracy of the k-Nearest Neighbors regression method in predicting the Jakarta Composite Index (JCI), thus adding evidence of the application of k-NN regression in predicting noisy financial time series data. The dataset consists of daily data from several time series variables in 2022. The result suggests that the accuracy of k-NN regression in predicting JCI fluctuation is very good, indicated by Mean Absolute Percentage Error (MAPE) being less than 2% for k = 2, 3, …, 10.
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Copyright (c) 2024 Najibullah, Azlina, Ricky Ariansyah, Lisnawati
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