Application Of The K-Medoid Method In Clustering Population Data By Subdistrict At The Population And Civil Registration Office Of Central Bengkulu Regency
Abstract
The application of the K-Medoid Method in grouping population data per sub-district at the Population and Civil Registration Office of Central Bengkulu Regency can provide information in the form of population density levels of sub-districts/villages in Central Bengkulu Regency which are divided into 3 groups, namely high, medium and low density levels, and can assist local governments in formulating more targeted policies. From the population data of 143 sub-districts/villages, data grouping analysis was carried out using the K-Medoid Method through the application that has been built, so that the results of the grouping of population data per sub-district/village which stopped at the 2nd iteration, namely a high population density level of 4 sub-districts/villages, a medium population density level of 17 sub-districts/villages, and a low population density level of 122 sub-districts/villages. Based on the tests that have been carried out, it is seen that the application of the K-Medoid Method can group population data per sub-district at the Population and Civil Registration Office of Central Bengkulu Regency which is summarized based on the data collection year.
Fields of Study & Subjects
Data Mining and Machine Learning Applications
Advanced Clustering Algorithms Research
Decision Support System Applications
Publication details
- Journal / source
- Jurnal Media Computer Science
- Volume
- 5
- Issue
- 1
- Pages
- 513–522
- Publication date
- 27 January 2026