Comparison of Machine Learning Algorithms for Soil Type Classification

被引：0

作者：

Harlianto, Pramudyana Agus ^{[1
]}

Setiawan, Noor Akhmad ^{[1
]}

Adji, Teguh Bharata ^{[1
]}

机构：

[1] Univ Gadjah Mada, Dept Elect & Informat Engn, Yogyakarta, Indonesia

来源：

2017 3RD INTERNATIONAL CONFERENCE ON SCIENCE AND TECHNOLOGY - COMPUTER (ICST) | 2017年

关键词：

Classification; Soil Type; Accuracy;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Machine learning algorithm can be applied for automating soil type classification. This paper compares several machine learning algorithms for classifying soil type. Algorithms that involve support vector machine (SVM), neural network, decision tree, and naive bayesian are proposed and assessed for this classification. Soil dataset is taken from the real data. Simulation is run by using RapidMiner Studio. The performance observed is the accuracy. The result shows that SVM, with the use of linear function kernel, outperforms the others algorithms. The SVM best accuracy is 82.35%.

引用

页码：7 / 10

页数：4

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