Clustering MSMEs in Jambi Province Using K-Means Algorithm for Policy Strengthening

Hetty Rohayani; Noneng Marthiawati; Saleh Yaakub

doi:10.35877/454RI.jinav4844

Authors

Hetty Rohayani Informatics Study Program, Faculty of Science and Technology, Muhammadiyah University of Jambi, Jambi, Indonesia
Noneng Marthiawati Information Systems Study Program, Faculty of Science and Technology, Muhammadiyah University of Jambi, Jambi, Indonesia
Saleh Yaakub Information Systems Study Program, Faculty of Science and Technology, Muhammadiyah University of Jambi, Jambi, Indonesia

DOI:

https://doi.org/10.35877/454RI.jinav4844

Keywords:

MSMEs, Jambi Province, K-Means Algorithm, Clustering, Policy Strengthening

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play an important role in supporting economic growth in Jambi Province. However, the distribution of MSMEs across regencies and cities has different characteristics, requiring further analysis to identify distribution patterns. This study aims to cluster MSME distribution in Jambi Province using the K-Means algorithm as an unsupervised machine learning approach. The study used MSME data from 2019–2023 obtained from the Regional Office of Cooperatives and Small Enterprises of Jambi. Data processing was conducted using Python through preprocessing, the Elbow Method, K-Means clustering, and cluster evaluation using the Silhouette Coefficient and Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters was three: Low Cluster (C0), Medium Cluster (C1), and High Cluster (C2). The Low Cluster included Merangin, Sarolangun, and Tebo Regencies, while the High Cluster included Batanghari, Kerinci, Muaro Jambi Regencies, and Jambi City. The evaluation results produced a Silhouette Coefficient value of 0.81 and a DBI value of 0.21, indicating that the clustering model has good quality and optimal cluster separation. This study can support regional development planning and targeted MSME policy formulation in Jambi Province.

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