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<p>This study compares the performance of four machine learning algorithms K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, and Random Forest in classifying community support for eco-friendly packaging among Millennials. The research addresses Indonesia’s waste management challenges, emphasizing the need for eco-friendly packaging. A 3-class dataset with 795 records and 13 features is analyzed, incorporating pre-processing techniques like Synthetic Minority Oversampling Technique and Principal Component Analysis. Random Forest consistently outperformed the other models, achieving 90.73% accuracy, 91.65% precision, 91.13% recall, and 90.43% F1 score in the 3-class configuration. Decision Tree achieved 86.52% accuracy, while KNN reached 82.02%. Naive Bayes had the lowest performance, with an accuracy of 76.97%. These results provide insights into machine learning applications for optimizing sustainable packaging strategies, aiding businesses and policymakers in promoting eco-friendly packaging solutions.</p>

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Comparative analysis of machine learning methods for classifying eco-friendly packaging usage among millennials

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • A. Turnip,
  • A. N. Q. Aina,
  • Sumarmi,
  • A. Dirpan,
  • Suhaeni,
  • Y. Deliana

摘要

This study compares the performance of four machine learning algorithms K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, and Random Forest in classifying community support for eco-friendly packaging among Millennials. The research addresses Indonesia’s waste management challenges, emphasizing the need for eco-friendly packaging. A 3-class dataset with 795 records and 13 features is analyzed, incorporating pre-processing techniques like Synthetic Minority Oversampling Technique and Principal Component Analysis. Random Forest consistently outperformed the other models, achieving 90.73% accuracy, 91.65% precision, 91.13% recall, and 90.43% F1 score in the 3-class configuration. Decision Tree achieved 86.52% accuracy, while KNN reached 82.02%. Naive Bayes had the lowest performance, with an accuracy of 76.97%. These results provide insights into machine learning applications for optimizing sustainable packaging strategies, aiding businesses and policymakers in promoting eco-friendly packaging solutions.