Circularity Assessment of Coffee Waste-Based Products with Supervised Learning Classification

Aulya Fadillah Sari; Devi Maulida Rahmah; Januardi

doi:10.37535/104006120261

Authors

Aulya Fadillah Sari Padjadjaran University
Devi Maulida Rahmah, S.T.P., M.T., Ph.D. Padjadjaran University
Januardi S.TP., MT., Ph.D. Padjadjaran University

DOI:

https://doi.org/10.37535/104006120261

Keywords:

circular economy, coffee waste, cascara, circularity indicator, logistic regression

Abstract

The rapid growth of the global coffee industry has led to increased coffee production and consumption, which consequently generates significant amounts of agro-industrial waste such as cascara. If not properly managed, these residues may contribute to environmental pollution and inefficient resource utilization. Converting coffee waste into value-added materials represents a promising strategy to support circular economy implementation. This study aims to evaluate the circularity performance of coffee waste-based composite products produced by Regoods by integrating circular economy indicators with supervised learning classification. A quantitative approach was applied using the Linear Flow Index (LFI) and Material Circularity Indicator (MCI), followed by logistic regression to analyze the influence of electricity consumption, cascara utilization, and polymer matrix composition. The dataset consists of six observations from three product categories: plates, cups, and boards. The results show high LFI values (0.8786-0.9204) and low MCI values (0.0796-0.1214), indicating a predominantly linear system due to the dominant use of virgin polymer materials. Among the products, cups exhibit the highest circularity performance. Polymer matrix usage significantly reduces circularity, while cascara utilization improves it. These findings highlight the importance of increasing secondary material utilization to enhance product circularity.

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