Clustering of Data Monitoring Water Quality Using Mean-Shift Clustering Method - Aidilof, Hafizh Al Kautsar; Rosnita, Lidya; Kurniawati, Kurniawati; Ikhwani, Muhammad (Journal of Computer Science, Information Technology and Telecommunication Engineering, 2025)
This study aims to cluster water quality data from Nile tilapia ponds using the Mean Shift Clustering method. The parameters used to analyze water quality include temperature, pH, turbidity, and salinity, which are crucial factors for the growth and health of Nile tilapia. The data used in this research consist of water quality measurements from several Nile tilapia ponds. The clustering process seeks to identify groups of data with similar water quality characteristics, providing insights into optimal environmental conditions for tilapia farming. The clustering results reveal several distinct groups of water quality based on variations in temperature, pH, turbidity, and salinity. Results of the experiment show that a bandwidth value of 400 successfully identifies a relatively simple number of clusters, specifically four clusters. The Mean Shift Clustering method proves effective in grouping data without requiring assumptions about data distribution and can detect clusters with arbitrary shapes. Consequently, the findings of this study can be used to provide recommendations for improving water quality to enhance tilapia pond productivity.