The Classification Status of River Water Quality in Riau Province Using Modified K-Nearest Neighbor Algorithm with STORET Modeling and Water Pollution Index

Abstract

The Department of Environment and Forestry, Pollution and Environmental Damage Control Division, has an active role in monitoring water quality in Riau Province. The rivers that are still monitored and managed are Kampar River, Siak River and Indragiri River. Division of Environment Pollution calculates river quality status manually using Microsoft Excel, this is not maximally done since this important information should be processed quickly. Division of water pollution must determine the right calculation to get the results of the water quality status. Because of many calculation formulas set by the government, the commonly used method is the STORET method and the Pollution Index. So, in overcoming the problem of classification, the researcher proposes the use of learning methods that can predict or determine the status of water quality with classification techniques on data mining that is Modified K-Nearest Neighbor (MKNN) which is a modification of K-NN. The calculation of the MKNN algorithm produced the highest accuracy of 85.10% at K = 5 using STORET result data as training data. While, using the Pollution Index data results, the highest accuracy is 76.92% at K = 1. Based on the analysis with attribute analysis, the attributes that influence the determination of river water quality are BOD, COD, NH3, Fecal Coli and Total Coli. This result can be taken into consideration by the Division of Environmental Pollution in the process of overcoming and reducing pollutant overload that exceeds quality standards.

Implementasi Algoritma Decision Tree dan Support Vector Machine (SVM) untuk Prediksi Risiko Stunting pada Keluarga

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Comparative Study of Algorithms Used in Water Pollution Prevention and Control

“…In a study conducted by Ramadhani [19] in Riau Province, Indonesia, the problem of water quality monitoring and classification was addressed. An improved K-nearest neighbor (MKNN) algorithm was used, which achieved a classification accuracy of 85.1%.…”

Water Pollutant Classification Method Based on Multi-Class Support Vector Machine

“…Shakhari [4] proposed a classification method to classify water quality data, and compared it with two existing classification methods (C-4.5 and logistic regression), and the experimental results verified the effectiveness of the method. Ramadhani [5] used an improved K-nearest neighbor algorithm (MKNN) for water quality monitoring and classification in Riau province, Indonesia, with a classification accuracy of 85.1%. Grbþiü [6] proposed a method for classifying pollutants in water supply networks based on random forest algorithm, and the proposed method has high accuracy in locating potential pollution sources.…”