Development Disparity Analysis: Clustering Regencies and Cities in Riau Province Based on Socio-Economic Indicators Using K-Means and Hierarchical Algorithms
DOI:
https://doi.org/10.62712/juktisi.v5i2.1454Keywords:
Clustering, K-Means, Hierarchical Clustering, Socio-Economic Indicators, Sdgs, Riau ProvinceAbstract
Inter-regional socio-economic disparity remains a persistent challenge in regional development across Indonesia, including Riau Province. Differences in economic capacity, human development quality, poverty levels, and labor absorption indicate that development characteristics across regencies and cities are not yet uniform. This study aims to classify regencies and cities in Riau Province based on socio-economic indicators for the 2019–2023 period using K-Means and Hierarchical Clustering algorithms, as well as to compare the most suitable method for representing the data structure. Secondary data utilized in this study consist of five main macro-indicators: Gross Regional Domestic Product (GRDP) per capita, poverty rate, open unemployment rate, Human Development Index (HDI), and mean years of schooling. The research stages involve data preprocessing, Min-Max normalization, outlier detection, clustering algorithm implementation, and evaluation using the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Score. The results demonstrate that the optimal number of clusters is two, where both algorithms yield identical evaluation values: a Silhouette Coefficient of 0.55, a Calinski-Harabasz Score of 34.45, and a Davies-Bouldin Index of 0.47. The clustering results reveal that Pekanbaru City forms a distinct, solitary cluster due to its superior macro-indicators, while the other eleven regencies and cities are grouped into the same cluster. This finding confirms a sharp socio-economic division between the provincial capital and its peripheral regions, which can simultaneously be utilized by the regional government as an empirical baseline evaluation to accelerate the achievement of SDGs Goal 1, Goal 4, and Goal 8 targets at the local level.
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