Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

*Deden Istiawan orcid  -  Akademi Statistika Muhammadiyah Semarang, Indonesia
Received: 17 Feb 2020; Revised: 9 Jul 2020; Accepted: 10 Jul 2020; Published: 31 Oct 2020; Available online: 27 Aug 2020.
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Abstract
Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.
Keywords: critical land; watersheds; classification algorithm; algorithm comparison
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