Clustering Provinces in Indonesia Based on Economic Indicators Using the K-Means Algorithm

Ilham Ilyasa; Muhamad Fazri Sugara; Abdul Aziiz; Rani Irma Handayani; Risca Lusiana Pratiwi; Euis Wida Nengsih

doi:10.59934/jaiea.v5i2.1812

Authors

Ilham Ilyasa Universitas Bina Sarana Informatika
Muhamad Fazri Sugara Universitas Bina Sarana Informatika
Abdul Aziiz Universitas Bina Sarana Informatika
Rani Irma Handayani Universitas Bina Sarana Informatika
Risca Lusiana Pratiwi Universitas Bina Sarana Informatika
Euis Wida Nengsih Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.1812

Keywords:

K-Means Clustering; Economic Development; Regional Inequality; Human Development Index; GRDP per Capita

Abstract

This study aims to analyze and classify the level of economic development in provinces in Indonesia using the K-Means algorithm. The data used includes three main indicators, namely Gross Regional Domestic Product (GRDP) per capita, percentage of poor population, and Human Development Index (HDI) in 2024 obtained from the Central Statistics Agency (BPS). The data was processed through normalization and analysis using the Elbow method to determine the optimal number of clusters. The results were evaluated using the Davies–Bouldin Index (DBI) to assess the level of separation and compactness between clusters. The results show that the most effective division consists of three groups representing high, medium, and low levels of development. Provinces such as DKI Jakarta and Riau are included in the high development cluster, Central Java and South Sulawesi are in the medium cluster, while Papua and East Nusa Tenggara are in the low cluster. These results show that machine learning methods, particularly K-Means, are capable of identifying patterns of regional economic inequality and provide a useful basis for the government in formulating more targeted and equitable development policies.

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