Implementation of the K-Means Algorithm for Gross Regional Domestic Product at Current Prices (Grdp Cp) Clustering Various Sectors City/District Businesses in Indonesia

Abstract

Aim/Purpose Economic development and growth are important indicators of a country's progress. Gross Regional Domestic Product at the sectoral level, known as GRDP CP (Gross Regional Domestic Product at Current Prices), provides valuable insight into the contribution of various business sectors to a country's economy.

Backgrounds The aim is to uncover hidden patterns and group sectors with similar economic characteristics, which can guide the development of sector-specific policies to encourage economic growth.

Methodology In this research method, we explored using the K-Means algorithm to group GRDP CP data from 17 city/district business sectors in Indonesia.

Contributions This research produced four classes, with the highest sector average scores being the agriculture, forestry, and fisheries sectors. In contrast, the lowest sectors were the water supply, waste management, and recycling.

Findings provide a more complete picture of the economic characteristics of each region and assist local governments in formulating more effective and targeted economic policies

Recommendations for Practitioners This research provides a new contribution to understanding economic characteristics at the local level in Indonesia. By focusing on the GRDP CP grouping of business fields in cities/districts, this research complements knowledge about regional economics. These results can be used as a basis for further study, including identifying factors that drive differences in economic clusters in various regions.

Recommendations for Researchers By applying the K-Means algorithm to GRDP CP data on business fields in cities/districts throughout Indonesia, this research provides a deeper understanding of the economic characteristics of each region. The results of grouping economic sectors reveal diverse growth patterns and policy implications that have the potential to support more focused and sustainable economic development.

Impact on Society For future research Grouping economic sectors based on Gross Regional Domestic Product at Current Prices (GRDP CP) data using the K-Means algorithm provides insights that can help in the formulation of more targeted and sustainable economic policies research has provided valuable insights, but several things could still be improved. For example, the data used may need to be revised regarding completeness and accuracy. In addition, this analysis does not consider external factors that can influence economic growth in certain regions. Therefore, future research could expand the study by incorporating additional data and considering other relevant factors.