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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering
Poningsih Poningsih, Poningsih
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Optimization Of Hybrid K-Means–Naïve Bayes Using Optuna for Classification of Global Plastic Waste Management Levels Madani, Aulya Fani; Poningsih, Poningsih; Almaida, Zulia; Saputra, Widodo
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5651

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.
Co-Authors Abdullah Ahmad Agus Perdana Windarto Akbari, Imam Almaida, Zulia Amalya, Nanda Ambariyanto Ambariyanto Batubara, Ela Roza Bintang, Daud Eka Irawan Hartama, Dedy Hartama, Dedy Ht. Barat, Ade Ismiaty Ramadhona Irawan Irawan Jalalludin, Jalalludin Lili Tanti Lumbantobing, Dedy Kristianto Madani, Aulya Fani Nainggolan, Joshua Fines Nasution, Siti Andry Yani Nur, Khairun Nisa Arifin Nur, Khairun Nisa Arifin Okprana, Harly Putri, Nirwanda Desnila Rahmat Widia Sembiring - Safii, M Siadari, Nina Helnida Sidauruk, Nadia Siregar, Abdul Zikri Siregar, Sandy Putra Solikhun Solikhun, Solikhun Sumarno . Sundari Retno Andani Sundari, Riski Wanto, Anjar Wibowo, Subastian Widodo Saputra Zulpani, Rahmat