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Arif, M. Arif Saputra
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Computer Science & IT

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Clustering of Aquaculture Productivity Villages in East Aceh Using the K-Means Algorithm Arif, M. Arif Saputra; Dinata, Rozzi Kesuma; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10102

Abstract

This study aims to classify villages based on the level of pond utilization and to develop a web-based application for categorizing aquaculture areas in East Aceh Regency. In contrast to traditional definitions based on harvest volume, this research defines productivity functionally—whether the pond area is actively managed or abandoned. The dataset consists of 146 villages and includes five primary variables: number of fish farmers, total pond area, number of pond plots, productive pond area, and abandoned pond area. Clustering was conducted using the K-Means algorithm, resulting in two main groups: productive and non-productive villages. Validation through the Silhouette Score revealed that using k = 2 yielded the highest score of 0.7576, indicating the most optimal clustering structure. The analysis showed that 92% of villages were categorized as productive, while 8% fell into the non-productive cluster. These two clusters differ significantly in terms of land utilization ratios and the number of active aquaculture workers. The findings not only offer a more refined spatial insight but also serve as a basis for the Department of Marine Affairs and Fisheries in formulating aquaculture zoning, revitalization programs, and more targeted resource allocation.
Co-Authors Rozzi Kesuma Dinata Yesy Afrillia