IMPLEMENTASI DATA ANALYTICS DENGAN ORANGE DATA MINING UNTUK PROYEKSI HARGA SAHAM (STUDI KASUS HARGA SAHAM PERBANKAN DI INDONESIA)
Keywords:
stock price projection, data science, net income, macro economicAbstract
The purpose of this research is to determine the accuracy of stock price predictions using analytical data using Orange Data Mining application based on the ARIMAX model. The research sample consists of banking stock in Indonesia, with codes BBCA, BBRI, BMRI, and BBNI, from Q1 2011 to Q2 2023. The four stocks were chosen due to their high valuations in Indonesia and are among the ten stocks with the largest valuations in the country. The best ARIMAX model was selected using statistical rules, and Orange application was used to test the model. Data from Q1 2011 to Q4 2022 was considered as training data, and predictions were made for Q1 and Q2 2023. The results showed high accuracy in stock price predictions with a consistently low MAPE value, which is always less than 10. This research suggests that investors can use Orange application for predicting stock prices based on analytical data.References
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