DIAGNOSA HAMA DAN PENYAKIT PADA TANAMAN PADI MENGGUNAKAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR

Abstract

This study builds systems Case Based Reasoning (CBR) to diagnose pests and diseases in rice plants using Naïve Bayes algorithm and K-Nearest Neighbor. CBR is one method of solving the problem with new cases of decision making based on the solution of previous cases by calculating the degree of similarity (similarity), The case consists of 13 species and 10 types of disease pests of rice plants. The degree of similarity can be determined by indexing and nonindexing. Indexing is the process of grouping the cases by classes that have been determined, while nonindexing a process without grouping cases. Based on cross validation testing using average values obtained accuracy of 92.88% to 153 test data on testing using the indexing and the average value of 89.63% accuracy of the test data in the test 153 using nonindexing.