Implementation of the K-Means Clustering Algorithm in City and Regency Clustering in North Sumatra Province Based on Small and Micro Industries
Agnes Irene Silitonga, Ali Akbar Lubis, Jufri Darma, Ferry Indra Sakti H. Sinaga, Yoakim Simamora

Universitas Negeri Medan

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

Topic: Applied Sciences and Information Technology