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Predicting Citation Dynamics and Mapping Research Trends in Nanocellulose: A Bibliometric and Machine Learning Approach (2021–2025) Edwin Kristianto Sijabat; Samsul Arifin
JURNAL INOVASI PENDIDIKAN DAN SAINS Vol 6 No 3 (2025): December
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Nahdlatul Wathan Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51673/jips.v6i3.2709

Abstract

This study aims to map research trends and predict citation dynamics in nanocellulose research within the materials science domain during 2021–2025. A quantitative bibliometric approach was employed using metadata retrieved from the Scopus database, followed by network visualization with VOSviewer and advanced data analysis using Python and machine learning techniques. A total of 2,971 publications were analyzed to identify publication patterns, collaboration networks, thematic evolution, and citation behavior. The results show that China dominates publication output and funding, while key journals and authors form highly interconnected citation networks. Topic modeling reveals emerging research fronts in biomedical hydrogels, nanocomposite films, and sustainable processing. Citation prediction using regression-based machine learning achieved moderate performance (R² = 0.23), indicating potential for early impact estimation. This study concludes that integrating bibliometrics with machine learning provides a comprehensive and predictive perspective on the evolving landscape of nanocellulose research and can support strategic research planning and policy decisions
Mapping Global Collaboration and Research Trends in Nanocellulose: A Bibliometric and Text Mining Analysis Edwin Kristianto Sijabat; Samsul Arifin
JURNAL INOVASI PENDIDIKAN DAN SAINS Vol 7 No 3 (2026): December
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Nahdlatul Wathan Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51673/jips.v7i3.2708

Abstract

Nanocellulose has emerged as a promising biomaterial with broad applications across sustainable packaging, biomedical engineering, and environmental remediation. Despite increasing global interest, a comprehensive understanding of the collaborative dynamics and evolving research directions in this field remains limited. This study aims to map the global research landscape of nanocellulose from 2021 to 2025 through an integrative bibliometric and text mining approach. A total of 2,971 scientific articles were retrieved from the Scopus database using a targeted query focused on nanocellulose-related keywords within the materials science domain. The metadata was analyzed using VOSviewer to visualize co-authorship networks, keyword co-occurrence, citation linkages, and bibliographic coupling. Complementary text analysis, including topic modeling via Latent Dirichlet Allocation (LDA), was conducted using Python libraries such as NLTK, spaCy, and Gensim. The results reveal China as the leading country in terms of research output, institutional collaboration, and funding, followed by India and the United States. Key thematic clusters center around biomedical applications, composite development, and water treatment. Highly cited authors and organizations are concentrated in specialized pulp and paper research institutes. This study not only delineates the structure and trajectory of global nanocellulose research but also highlights emerging areas and collaboration patterns. The findings can guide future research investments, inform policy decisions, and foster strategic academic partnerships in the rapidly evolving field of cellulose-based nanomaterials