• Title of article

    Features Analysis of the Research and Development Industry in Indonesia

  • Author/Authors

    Febrian Khusnul Hidayati, Endang Indonesian National Research and Innovation Agency, Indonesia , Sartono, Bagus Department Statistics and Data Science - IPB University, Indonesia , Mohamad Soleh, Agus Department Statistics and Data Science - IPB University, Indonesia

  • Pages
    11
  • From page
    55
  • To page
    65
  • Abstract
    R&D is one of the key drivers of technological progress and contributes to increased productivity and profit growth. Indonesian percentage of Gross Domestic Expenditure on R & R&D (GERD) to GDP in 2018 is one of the Global Competitiveness Index indicators, only reaches 0.28% and is dominated by the government sector, while the industrial sector is only 7.34%. One of the reasons for this small value is that the data collection of R&D on the business sector in Indonesia has not been carried out optimally. A classification model is needed to determine the data collection target so that the results are more optimal. The main objective of this study is to classify R&D industries actors in Indonesia using XGBoost and then analyze the features for R&D industries actors using SHAP. XGBoost is one of the black-box models that is difficult to interpret, and SHAP is one of the interpretation methods. The classification results using XGBoost obtained the accuracy, AUC, and F1-Score values of 79.61%, 0.7646, and 84.44%, respectively. Based on the Shapley value of the SHAP method, it was found that the average growth in R&D expenditure had the highest contribution. The feature's contribution to the estimation will be even higher if the mean of R&D expenditure growth is higher (more than 0). The other one is the ratio of researchers to R&D human resources. If the ratio is more than 75%, it will negatively contribute. Finally, exports and State-Owned Enterprise (BUMN) feature with the smallest contribution.
  • Keywords
    Research and Development (R&D) , Industry , XGBoost , SHAP , Feature Analysis
  • Journal title
    International Journal of Information Science and Management (IJISM)
  • Serial Year
    2022
  • Record number

    2729864