Aulya Fani Madani, Poningsih Poningsih, Zulia Almaida, Widodo Saputra

Optimization Of Hybrid K-Means–Naïve Bayes Using Optuna for Classification of Global Plastic Waste Management Levels

Introduction

Optimization of hybrid k-means–naïve bayes using optuna for classification of global plastic waste management levels . Improve global plastic waste management level classification with an Optuna-optimized hybrid K-Means–Naïve Bayes model. Achieve 95% accuracy for large environmental datasets, supporting data-driven policy.

Abstract

The rapid growth of plastic waste has become a serious global environmental challenge, while existing waste management analysis methods often struggle to handle large and heterogeneous environmental datasets. This study aims to improve the classification of global plastic waste management performance by integrating K-Means clustering and Naïve Bayes with Optuna-based hyperparameter optimization. Using a dataset of global plastic waste indicators from multiple countries during 2020–2024, K-Means is first applied to generate waste management level clusters, which are then classified using Naïve Bayes. The hybrid model is further optimized by tuning the var_smoothing parameter using Optuna. Experimental results show that the hybrid approach improves classification performance compared to the baseline Naïve Bayes model, while the optimized model increases accuracy from 89% to 95% along with improvements in precision, recall, F1-score, and ROC-AUC. These results indicate that combining clustering-based labeling with automated hyperparameter optimization can enhance the reliability of machine learning models for large-scale environmental data analysis. Therefore, the proposed approach can support more accurate evaluation of global plastic waste management and assist data-driven environmental policy development.

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