Analysis of Data Mining in the Group of Water Pollution Areas using the K-Means Method in Indonesia

Abstract

Water is an essential requirement in human life. However, pollution causes the water quality to become poor, making it unsuitable for use. Pollution comes from rubbish and waste dumped into rivers, lakes and other water areas. This study aims to carry out a model for mapping areas contaminated with water pollution using artificial intelligence techniques. The data sample comes from the Indonesian Central Statistics Agency (abbreviated as BPS) which consists of 34 records. The data used are provinces in Indonesia that are polluted by water pollution in rural areas. The intelligence technique used is data mining using the k-means method. The variable used is the number of polluted villages by province. The mapping label used is the high cluster (K1) for water pollution and the low cluster (K2) for water pollution. Analysis using Rapid Miner software. The results showed that 4 provinces were included in the high cluster (K1) category, namely North Sumatra, West Java, Central Java, East Java. Testing of cluster results was carried out using Davies Bouldin (k = 2) with a value of 0.328, which means that the cluster results created were optimal. The results of the analysis are expected to be input for the government in focusing on areas polluted with water pollution.

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Application of unsupervised clustering model based on graph embedding in water environment

“…Li et al proposed a comprehensive model based on K-means cluster analysis and set pair analysis, applied the model to the reservoir area to conduct water pollution risk assessment, and realized the use of cluster analysis method to determine the source of pollution 12 . Jatnika et al applied the k-means method to the mapping of water pollution areas in Indonesia, and passed the analysis results to government departments to focus on the management of water pollution areas 13 . Birant et al proposed a new density-based clustering algorithm ST-DBSCAN and applied it to ocean data to explore areas with similar seawater characteristics 14 .…”

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