Analysis of Cantaloupe Fruit Maturity Based on Fruit Skin Color Using Naive Bayes Classifier

Abstract

The traditional sorting of fruit maturity can be done by seeing the color of the fruit’s skin. Manual sorting will take a long time and the results are subjective. This paper presents the results of maturing cantaloupe fruit based on the color of the fruit skin using a digital image of the fruit skin. The research objective is to classify the maturity of cantaloupe fruit using the Naive Bayes Classifier method and compare the results with similar studies using the Learning Vector Quantization (LVQ) Artificial Neural Network method. This study used the image of a raw and mature cantaloupe rind of 15 images each. A total of 16 images are grouped into training data for the training process and 14 other images are grouped into test data for the testing process. The results showed that the accuracy of training and testing using the Naive Bayes Classifier method was 68.75% and 57.14%, respectively. The accuracy of the training and testing of the Naive Bayes Classifier method turns out to be lower compared to the LVQ Artificial Neural Network method.