An Approach to Classify Rice Quality using Electronic Nose Dataset-based Naïve Bayes Classifier

Rice is a staple cuisine for people in Asia, particularly in Indonesia. Rice reserves that are optimal provide sufficient dietary needs. The fundamental issue is that rice quality has deteriorated in recent years, resulting in losses. The traditional method for determining rice quality is to utilize human senses to detect the aroma of rice and examine the texture of the rice. In this paper, we use electronic nose dataser to predict rice quality using the Naïve Bayes classifier methods; Gaussian, Bernoulli, Multinomial, and Complement. Among these four methods, Multinomial obtains an accuracy of 97%, Complement obtains an accuracy of 98%, and Gaussian obtains an accuracy of 82%, these methods showed a high ROC value of 1.00, indicating perfect classification. With a ROC value of 0.54, the Bernoulli method exhibited the least performance in classifying the rice quality by obtaining an accuracy of 52%• The managerial implications of these findings are discussed.