# Ensemble Method of Triple Naive Bayes for Plastic Type Prediction in Sorting System Automation
## 4 Comparison with Other Studies  
Several other works that build intelligent computing systems for sorting plastic waste based on machine learning are presented in Table 10 as a comparison. The majority of the works use the validation method using k-fold cross-validation. In work [4], the precision metric that describes the percentage of objects that are truly the actual plastic type from all objects predicted as that type is high, which is more than 90%. Nevertheless, the accuracy, which is defined as the proportion of observations that are accurately classified in all plastic varieties, is low at 45.5%. Likewise, the recall that describes the percentage of observations in a plastic type that are successfully predicted as the same plastic type shows a low value (47.64%). The work also does not use a validation method in performing its prediction task. In work [12], which validates the prediction performance using 10-fold cross-validation and obtains a performance of 100%, in addition to not being generalized, the number of samples of 20 indicates that the test data of only four allow for as many true predictions as the test data so that the performance metric easily achieves 100%. Consequently, the model's efficacy is also influenced by the quantity of samples. We have shown that the performance of k-fold cross-validation can be different in each sampling, as given in Figure 6; therefore, it is necessary to generalize the performance of the prediction model built for a machine learning-based system of at least thirty samplings [33]. The difference between the method we propose and the current method is that the ensemble method we propose is built from single methods in the form of NB methods that implement discretization, both crisp and fuzzy. Our proposed method has also proposed generalization to the performance of all proposed models, both the single NB method and the ensemble of the triple NB method, and obtained satisfactory performance (more than 97%). According to [47, 48, 49], a performance above 85% is considered satisfactory. We built an intelligent computing system that has high, stable, and robust performance, as proven using statistical tests presented in Table 8, with accuracy, precision, and recall metric indicators all above 97% (Table 7). The main advantage of using our proposed method with high, stable, and robust performance is that the automatic plastic sorting process can run effectively and efficiently. The industry can use this resulting method to build automatic sorting computations. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Comparison Model**} & \multicolumn{3}{c}{**Absolute Mean Difference**} \\ \cline{2-4}  & **Accuracy** & **Recall** & **Precision** \\ \hline NB with crisp discretization vs. NB with linear–triangular & 0.02 & 0.02 & 0.02 \\ NB with crisp discretization vs. NB with linear–trapezoidal & 0.03 & 0.03 & 0.03 \\ NB with crisp discretization vs. ensemble of triple NB & 0.02 & 0.02 & 0.02 \\ NB with linear–triangular vs. NB with linear–trapezoidal & 0.01 & 0.01 & 0.01 \\ NB with linear–triangular vs. ensemble of triple NB & 0.04 & 0.04 & 0.04 \\ NB with linear–trapezoidal vs. ensemble of triple NB & 0.06 & 0.05 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Tukey–Kramer test of single and ensemble methods.