Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Advanced Computer Knowledge and Algorithms
Fairuz Aqimi
Universitas Malikussaleh
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Geospatial Mapping of Plantation Products in Bireuen Regency Using the K-Means Clustering Method Fairuz Aqimi; Asrianda; Ar Razi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 4 (2026): Journal of Advanced Computer Knowledge and Algorithms - October 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i4.28112

Abstract

This study aims to implement the K-Means Clustering algorithm for geospatial mapping of plantation commodities in Bireuen Regency. The dataset consists of 255 records collected from 17 sub-districts during the 2020–2024 period, obtained from the Bireuen Regency Plantation Office. The analyzed commodities include rubber (Hevea brasiliensis), nutmeg (Myristica fragrans), and sugar palm (Arenga pinnata). The clustering variables comprise the area of immature plants (TBM), productive plants (TM), damaged plants (TR), production yield, and productivity per hectare. The data were normalized using the Min-Max Scaling method before being grouped into three clusters (low, medium, and high) using the K-Means algorithm. Clustering performance was evaluated using the Silhouette Score and Davies–Bouldin Index (DBI). The results indicate that the sugar palm commodity achieved the best clustering performance, with Silhouette Scores ranging from 0.7493 to 0.8824 and DBI values from 0.0000 to 0.4080. Rubber produced its best result in 2021 with a DBI of 0.4705, while nutmeg also demonstrated good clustering quality. The clustering results were visualized as geospatial choropleth maps using GeoPandas and integrated into a Flask-based Web GIS. The findings demonstrate that the proposed system effectively supports plantation commodity classification and provides reliable geospatial information to assist data-driven plantation planning and resource allocation in Bireuen Regency.
Co-Authors Ar Razi Asrianda Asrianda