Classification of diseases for rice plant based on Naive Bayes classifier with a combination of PROMETHEE

Abstract

Rice is a staple food crop for most of the population in Indonesia. The need for food, such as rice, is increasing every year. However, in recent years, the low productivity is due to the prolonged dry season. Also, quality rice production can be influenced by several factors, such as the presence of pests and diseases that attack rice plants so that farmers have difficulty dealing with them. Most of the researchers have applied a measure with an intelligence-based measurement; however, the obtained accuracy cannot achieve maximum. Therefore, in this research, a new approach was carried out using the Naive Bayes and PROMETHEE method to determine diseases and pests in rice that have been proposed to reduce the risk of errors and shorten the time in decision making. The contribution of this research is to identifying these problems, including the search for prior probability, conditional probability, posterior probability, and ranking that can use the Naive Bayes and PROMETHEE method to result the highest accuration value. So, this research can be use rapid decision making based on learning data. From the sample's data of the Agriculture Office of Lamongan Regency, East Java pointed out that 38 symptoms can cause 13 types of diseases and rice plants' pests based on the learning process has 73.91% accuracy with k=3. Testing the system used data on pest and disease disturbances as many as 180 data and the data division using k is 4. It proves that the naive bayes and PROMETHEE method is able to give better results.