Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest


  • Fitri Khairani IPB University
  • Anang Kurnia IPB University, Indonesia
  • Muhammad Nur Aidi IPB University, Indonesia
  • Setia Pramana Politeknik, Statistika STIS, Jakarta, Indonesia




Classification, Economy, GDP, Online News, Prediction, Random Forest


Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using  Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category

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How to Cite

Khairani, F. ., Kurnia, A. ., Aidi, M. N. ., & Pramana, S. . (2022). Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 532-540.