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Proceedings of the 7th International Conference on Innovation in Education, Science, and Culture, ICIESC 2025, 16 September 2025, Medan, Indonesia

Research Article

Implementation of the K-Means Clustering Algorithm in City and Regency Clustering in North Sumatra Province Based on Small and Micro Industries

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  • @INPROCEEDINGS{10.4108/eai.16-9-2025.2361141,
        author={Agnes  Irene Silitonga and Ali  Akbar Lubis and Jufri  Darma and Ferry Indra Sakti H. Sinaga and Yoakim  Simamora},
        title={Implementation of the K-Means Clustering Algorithm in City and Regency Clustering in North Sumatra Province Based on Small and Micro Industries},
        proceedings={Proceedings of the 7th International Conference on Innovation in Education, Science, and Culture, ICIESC 2025, 16 September 2025, Medan, Indonesia},
        publisher={EAI},
        proceedings_a={ICIESC},
        year={2026},
        month={3},
        keywords={k-means clustering algorithm small and micro industries clustering machine learning},
        doi={10.4108/eai.16-9-2025.2361141}
    }
    
  • Agnes Irene Silitonga
    Ali Akbar Lubis
    Jufri Darma
    Ferry Indra Sakti H. Sinaga
    Yoakim Simamora
    Year: 2026
    Implementation of the K-Means Clustering Algorithm in City and Regency Clustering in North Sumatra Province Based on Small and Micro Industries
    ICIESC
    EAI
    DOI: 10.4108/eai.16-9-2025.2361141
Agnes Irene Silitonga1,*, Ali Akbar Lubis1, Jufri Darma1, Ferry Indra Sakti H. Sinaga1, Yoakim Simamora1
  • 1: Universitas Negeri Medan, North Sumatera, Indonesia
*Contact email: agnesirenesilitonga@unimed.ac.id

Abstract

Small and micro industries have a strategic role in the regional economy, especially in improving people's welfare and driving economic growth. This study aims to cluster regencies and cities in North Sumatra Province using the K-Means Clustering algorithm. The data used include the number of business units, workforce, and bank capital loans in each region. The K-Means Clustering method was chosen because of its ability to cluster data based on similar characteristics so that it can provide an overview of the classification of regions with similar small and micro industry potential. The results of the study show that regencies and cities in North Sumatra Province can be clustered into three clusters, namely low, medium, and high clusters. The results of this clustering are expected to be the basis for local governments in designing more effective policies for the development of small and micro industries in each region, such as the allocation of capital assistance, training, and strengthening the industrial supply chain.

Keywords
k-means clustering algorithm, small and micro industries, clustering, machine learning
Published
2026-03-18
Publisher
EAI
http://dx.doi.org/10.4108/eai.16-9-2025.2361141
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