# Ensemble Method of Triple Naive Bayes for Plastic Type Prediction in Sorting System Automation
## 1 Introduction  
The use of plastic is becoming more widespread in countries experiencing economic expansion [1]. Since 2000, plastic waste has more than doubled [2]. The 2023 Indonesian River Affairs Research Agency census conducted in 13 provinces shows that plastic waste is a major problem in Indonesia. The nature of plastic waste, which is difficult to decompose, has a very broad negative impact on various ecosystems, human health, and many species [3, 4]. Recycling has been identified as a viable alternative for plastic waste management [5]. However, on the other hand, it can cause cross-contamination between types of plastic, thereby increasing industrial operational costs [6, 7]. In the industrial process, plastic waste recycling requires an automatic sorting system to sort plastic waste based on its type. This system requires intelligent computing that should not only consider advanced technology in obtaining data, the number of samples, and the validation techniques used but alsoconsider that the performance of the prediction model obtained is not only high but also robust, not because of luck or coincidence due to sampling carried out in the training and testing process. High, stable, and robust prediction performance will provide an effective and efficient sorting system [8; 9]. In the current era of Industry 4.0, the use of digital images extracted into the chromatic model of Red, Green, and Blue (RGB) as datasets in solving prediction or identification-related problems is increasing rapidly because it is cheaper than other technologies [10], such as multi-wavelength transmission spectrum [7], image sensor [4], laser [11], or infrared sensor [12; 13; 14]. Some infrared sensor technologies that use spectroscopy are claimed to be low cost, but the prediction performance of the intelligent system that is built is still not satisfactory, with performance metrics below 73% [14], or the performance obtained uses very few samples [7; 12], or the performance obtained is not yet robust because it is only based on one sampling [4; 7; 12; 13; 14]. Digital images processed using the chromatic model of RGB are generally more informative [15], have a real difference between the channel colors [16; 17], and can also provide good predictive performance [18; 19; 20]. However, not all prediction methods in machine learning can directly process ratio-scale data generated by digital image processing to the chromatic model of RGB. In addition, the process of transforming ratio data to ordinal or nominal can produce ambiguous interval groups [21], especially digital image data processed into the RGB color space model [22; 23], so the transformation carried out becomes inefficient [24]. Fuzzy discretization can overcome the problem of ambiguity in datasets [25; 26], and in addition, it has the potential to enhance the prediction method's efficacy [27; 28]. Nevertheless, the prediction model's efficacy with fuzzy discretization is significantly influenced by the combination of fuzzy membership functions employed [22; 29]. The main challenge in building and developing intelligent computing is to obtain high, stable, and robust prediction performance. The ensemble method is a method that combines several single prediction methods based on machine learning into an algorithm to obtain better performance [30]. This method is one of the solutions when a single prediction method does not provide satisfactory prediction performance [31]. This method can also reduce prediction variance [32] and increase the robustness of the performance of a single method [33; 34]. However, not all combinations of single prediction methods into ensemble methods provide good performance [31; 34]. In-depth research is needed regarding the combination of single prediction methods that build it. For this reason, this study aims to build and develop intelligent computing for the sorting system automation of plastic waste based on digital images using an ensemble method constructed from three basic prediction methods based on the Naive Bayes (NB) model.