PREDICTION OF PLASTIC-TYPE FOR SORTING SYSTEM USING DECISION TREE MODEL

Authors

  • Astuti Astuti Department of Mechanical Engineering, Faculty of Engineering, Universitas Sriwijaya, Sumatera Selatan, Indonesia
  • Anthony Costa Department of Civil Engineering, Faculty of Engineering, Universitas Sriwijaya, Sumatera Selatan, Indonesia
  • Akbar Teguh Prakoso Department of Mechanical Engineering, Faculty of Engineering, Universitas Sriwijaya, Sumatera Selatan, Indonesia
  • Irsyadi Yani Department of Mechanical Engineering, Faculty of Engineering, Universitas Sriwijaya, Sumatera Selatan, Indonesia
  • Yulia Resti Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Sriwijaya, Sumatera Selatan, Indonesia

DOI:

https://doi.org/10.51630/ijes.v4i1.86

Keywords:

Decision tree, Discretization, Plastic-Type, Prediction

Abstract

Plastic is the most widely used inorganic material globally, but its hundred-year disintegration time can harm the environment. Polyethylene Terephthalate (PET/PETE), High-Density Polyethylene (HDPE), and Polypropylene are all commonly used plastics that have the potential to become waste (PP). An essential first step in the recycling process is sorting out plastic waste. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. This paper proposes the Decision Tree model to predict the three plastic-type sorting systems based on discretizing predictor variables into two and three categories. The resampling method of k-fold cross-validation with ten folds for less biased. Discretization of the predictor variables into three categories informs that the proposed decision tree model has higher performance compared to the two categories with an accuracy of 81.93 %, a recall-micro of 72.89 %, a recall-macro of 72.30 %, a specificity-micro of 86.45%, and the specificity-macro of 86.51%, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

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Published

2023-03-10

How to Cite

Astuti, A., Costa, A., Prakoso, A. T., Yani, I., & Resti, Y. (2023). PREDICTION OF PLASTIC-TYPE FOR SORTING SYSTEM USING DECISION TREE MODEL. Indonesian Journal of Engineering and Science, 4(1), 075–081. https://doi.org/10.51630/ijes.v4i1.86