Ensemble Method of Triple Naïve Bayes for Plastic Type Prediction in Sorting System Automation

Recycling has been acknowledged as a viable alternative for the management of plastic refuse. An automatic sorting system is required by the industry to predict the plastic waste based on the type before it is recycled. The plastic sorting system automation requires intelligent computing as a software system that can predict the type of plastic accurately. The ensemble method is a method that combines several single prediction methods based on machine learning into an algorithm to obtain better performance. This study aims to build intelligent computing for the automation of digital image-based plastic waste sorting systems using an ensemble method built from three naïve Bayes single prediction methods. The three single models consist of one Naïve Bayes (NB) model with crisp discretization and two NB models with fuzzy discretization, namely those using a combination of linear–triangular fuzzy membership functions and a combination of linear–trapezoidal fuzzy membership functions. We hypothesize that the performance of each single model and the proposed ensemble model is different, and the performance of the ensemble model is higher than all the single models used to build it. The hypothesis is proven, and there is an increase in performance from each single method to the ensemble method ranging from 2.06% to 5.56%. The evidence of this hypothesis also shows that the performance of the proposed prediction model using the ensemble method built from three naive Bayes models is high and robust.