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Arya Kusuma
Dian Nuswantoro University

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Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction Arya Kusuma; De Rosal Ignatius Moses Setiadi; M. Dalvin Marno Putra
Journal of Applied Intelligent System Vol 3, No 1 (2018): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v3i1.1988

Abstract

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
Co-Authors De Rosal Ignatius Moses Setiadi M. Dalvin Marno Putra