Comparison of K-Nearest Neighbor and Naïve Bayes Classification Methods for Coconut Maturity Data
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Abstract
Classification of coconut maturity level is an important aspect in the coconut industry to ensure product quality and optimal selling value. This study aims to compare the performance of two data mining classification methods, namely K-Nearest Neighbors (KNN) and Naïve Bayes, in classifying coconut maturity levels in Tembilahan, Indragiri Hilir Regency. The dataset used consists of 300 data with parameters such as skin color, fruit weight, diameter, and fruit texture condition. Evaluation was conducted using accuracy, precision, recall, F1-score, and Cohen's Kappa metrics with 80:20 data division for training and testing. The results showed that the Naïve Bayes method provided superior performance with an accuracy of 86.67% and a Cohen's Kappa value of 0.795 compared to KNN which only achieved an accuracy of 43.33% and a Cohen's Kappa value of 0.144. Naïve Bayes also showed better consistency in classifying the three categories of coconut maturity (young, semi-old, and old) with an average F1-score of 0.8455 versus 0.4119 for KNN. This study provides a recommendation that the Naïve Bayes method is more effective for coconut maturity classification, especially in data conditions that are not fully optimized in terms of preprocessing.
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