Comparison Naïve Bayes and SVM to Classify Drought-Infected Rice Plants Based on Morphological Characteristics in Supporting National Food Security

Data mining is part of the Knowledge Discovery in Database (KDD) process. The use of data mining serves to classify, predict, and extract other useful information from large data sets. This study aims to classify rice plants under treatment (drought stress and control) using data mining, focusing on the analysis of the variables of Leaf Area (LA), Root Length (RL), and Shoot Length (SL). Each classification algorithm has different characteristics, resulting in varied performance results. After testing both classification algorithms, the accuracy results were 71.70% for Naïve Bayes and 73.85% for SVM. This shows that the SVM algorithm performs better than Naïve Bayes algorithms to determine best treatment of rice to support national food security further. Furthermore, It also can be concluded that using a machine learning approach can solve problems in the classification of rice plants affected by drought threats is fairly effective with the maximum score obtained is only 73.85%.