Application of Fuzzy C-Means Algorithm for Clustering Fast Moving and Slow Moving Printing Materials at Satria Print MSME

Kosim; Asep Kosasih; Arjun Satria Halmahera Dwi Putra

doi:10.58421/misro.v5i3.2081

DOI:

https://doi.org/10.58421/misro.v5i3.2081

Authors

Kosim Sekolah Tinggi Ilmu Komputer Poltek Cirebon https://orcid.org/0009-0008-2903-1667
Asep Kosasih Sekolah Tinggi Ilmu Komputer Poltek Cirebon
Arjun Satria Halmahera Dwi Putra Sekolah Tinggi Ilmu Komputer Poltek Cirebon

Keywords:

Data mining, Fuzzy clustering, Inventory classification, Micro, small, and medium enterprises, Printing materials

Abstract

Inventory decisions in micro, small, and medium enterprises (MSMEs) are often based on experience rather than on systematic analysis of transaction records. This study applies the Fuzzy C-Means (FCM) algorithm to classify printing materials at Satria Print MSME into Fast Moving and Slow Moving groups. Fifty material records from January to March 2026 were described by sales quantity, transaction frequency, and final stock. After Min–Max normalization, FCM was run with two clusters, a fuzziness exponent of 2, and a stopping tolerance of 0.0001, and each material was assigned to the cluster with its largest membership degree. The algorithm converged after 11 iterations. Seventeen materials (34%) were classified as Fast Moving and 33 (66%) as Slow Moving. The strongest Fast Moving memberships were found for F4 70 gsm HVS paper (0.919), 150 gsm art paper (0.906), A4 thermal laminating film (0.886), and Epson yellow ink (0.859), indicating that core paper, laminating, and ink supplies deserve priority in stock monitoring. Because the variables were strongly correlated and final stock is not an unambiguous indicator of movement, the result is a first-stage segmentation that complements turnover-based analysis. Future studies should validate the number of clusters and use longer, multi-business datasets.

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