静音水识别中的单一分类方法与集成模型

Prasetya Widiharso, Siti Sendari, Anik Nur Handayani, Nastiti Susetyo Fanani Putri
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引用次数: 0

摘要

需要根据娱乐设施、牲畜、渔业和种植园的需求对水质进行分类,以根据国家水质标准确定利用率。本研究中使用的方法有K-最近邻(KNN)、支持向量机(SVM)、朴素贝叶斯(NB)和集合模型。测量的参数包括温度、TDS、TSS、pH、COD、BOD、DO和降雨量。本研究的主要目的是发现单一分类方法和集成模型对具有不平衡类分布的数据类型的性能。分类对象分为两类。首先,是指定娱乐设施、渔业和畜牧业的类别。第二,阶级分配农作物耕种。KNN的应用测试结果获得了86%,SVM获得了87%,NB获得了90.57%。同时,通过集成模型,获得了94.43%的Bagging分类器、94.96%的Gradient Boosting分类器和95.94%的Adaboost分类器
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Performa Metode Klasifikasi Tunggal dan Ensemble Model dalam Identifikasi Baku Mutu Air
Water quality classification for the needs of recreational facilities, livestock, fisheries, and plantations is needed to determine utilization based on water quality according to national water quality standards. The methods used in this research are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), and Ensemble Model. The parameters measured consisted of temperature, TDS, TSS, pH, COD, BOD, DO, and rainfall. The main objective of this research is to discover the performance of a single classification method and ensemble model on data types with unbalanced class distributions. Classification objects are divided into two classes. First, is the class for the designation of recreational facilities, fisheries, and livestock. Second, the class for the allotment of crop cultivation. The test results of the application of the KNN obtained 86%, SVM obtained 87%, and NB obtained 90.57%. Meanwhile, through the ensemble model, the results obtained are 94.43% Bagging Classifier, 94.96% Gradient Boosting Classifier, and 95.94% Adaboost Classifier
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0.00%
发文量
30
审稿时长
12 weeks
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