Open AccessTomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction
Author(s) -
Arya Aji Kusuma,
De Rosal Ignatius Moses Setiadi,
M. Dalvin Marno Putra
Publication year - 2018
Publication title -
jais (journal of applied intelligent system)
Language(s) - English
Resource type - Journals
eISSN - 2503-0493
pISSN - 2502-9401
DOI - 10.33633/jais.v3i1.1988
Subject(s) - naive bayes classifier , histogram , pattern recognition (psychology) , artificial intelligence , classifier (uml) , computer science , feature extraction , bayes classifier , bayes' theorem , mathematics , support vector machine , image (mathematics) , bayesian probability
Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy