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Predicting the Quality of Pineapple Using the Naive Bayes Classifier Method

The growing demand for pineapple-based snacks offers very promising business prospects for fruit growers in Indonesia. This opportunity has been taken advantage of by many local farmers who grow this uniquely shaped fruit. To simplify the process of analyzing the quality of pineapples worth selling, machine learning assistance is needed. The factors that will be used for the calculations and analyzes that were previously performed manually by humans, should be performed with machine learning more quickly, efficiently, effectively and accurately. The author proposes the Naïve Bayes Classifier model as a classification method to demonstrate that the model has good accuracy, good precision, good recall and is good at classifying data. In this proposed model, the Naïve Bayes Classifier is a prediction technique based on simple probabilistic criteria and on the application of Bayes’ theorem with the assumption of strong independence. Therefore, this model is capable of producing high classification accuracy with low complexity with classification accuracy up to 75%, so this model can be efficient as an analytical tool.