Implementation of the K-Means Algorithm to Determine the Classification of River Water Quality in Jakarta Based on Chemical Parameters

Abstract

This study aims to implement the K-Means algorithm to classify river water quality in Jakarta based on key chemical parameters: Biochemical Oxygen Demand (BOD), nitrate, and nitrite levels. Water quality is a critical issue in Jakarta due to industrial activities and domestic waste contributing to pollution, which poses risks to public health and ecosystems. Data were collected from various monitoring points along the rivers, focusing on the mentioned parameters. The K-Means algorithm was applied to classify the water samples into categories: good, moderate, and poor quality. Results showed that high BOD levels were strongly associated with poor water quality, indicating organic pollution. Elevated nitrate and nitrite levels also contribute to water degradation, reflecting impacts from agricultural runoff and wastewater. The clustering results revealed that the water quality in Jakarta's rivers is predominantly poor, especially in areas with high BOD levels, which indicates organic pollution from domestic and industrial waste. The study demonstrates the K-Means algorithm's effectiveness in analyzing water quality data and suggests its potential as a valuable tool for environmental monitoring. The findings highlight the need for enhanced water quality management in Jakarta and provide a foundation for future research to integrate more parameters and time-based data to better understand trends and support decision-making in pollution control.