| Abstrakt: |
Plastic is the most often used inorganic material in the world, and its 100-year environmental degradation time can be hazardous. High-Density Polyethylene (HDPE), Polypropylene, and Polyethylene Terephthalate (PET/PETE) are all frequently used polymers that could end up in the waste stream (PP). Sorting plastic waste is a crucial first step in the recycling process. Preparing testing materials in the form of the plastic bottle waste types PET (Polyethylene Terephthalate), PP, and HDPE is the first step in identifying plastic bottle trash (Polypropylene). With 30 varieties of PET plastic, 30 types of HDPE plastic, and 30 kinds of PP plastic, respectively, 90 different types of plastic bottle waste are used. Digital image data in the RGB color space can be used as a low-cost automated plastic sorting system dataset, and learning datasets can forecast the kind. Based on the discretization of predictor variables into three categories, these studies show the Nave Bayes method for estimating the three plastic-type sorting systems. The suggested Naive Bayes model has an accuracy of PP of 70%, PET of 73,33%, and HDPE of 73,33% according to the discretization of the predictor variables into three categories. [ABSTRACT FROM AUTHOR] |
