Matematika dalam Penelitian Kopi: Visualisasi Jaringan dan Klasterisasi Topik Berdasarkan Data Scopus

Abstract

This study aims to explore the scientific landscape of mathematical applications in coffee-related research using a bibliometric approach. By analyzing 99 documents retrieved from the Scopus database, this study identifies global research trends, productive authors and institutions, country-level contributions, and dominant subject areas. The analysis includes publication patterns, keyword co-occurrence networks, and author collaboration clusters visualized using VOSviewer. The findings show a significant rise in scientific interest since 2016, with the United States and Indonesia leading in publication volume. Although the research spans multiple disciplines such as mathematics, engineering, agriculture, and computer science, collaboration among researchers remains limited, with many author clusters operating independently. The keyword clustering reveals six major themes ranging from bioinformatics and plant disease modeling to chemical composition and teaching philosophy. These findings underscore the growing role of mathematical methods in coffee research and highlight the need for stronger interdisciplinary and international collaboration to support innovation in coffee science and production.