Comparison of Discrete Adaptive Boosting Algorithms for Classification and Regression Tree and Naive Bayes in Pistachio Nut Classification
Abstract
Machine learning is an effective tool for identifying and classifying various conditions, such as predicting shoe sales, classifying raisin types, classifying fruit productivity, and so on. This technique is widely used in various sectors. One example is pistachio sorting. In some places, pistachio sorting is still done traditionally by humans. This is disadvantageous because the costs tend to be high, and the sorting process becomes inconsistent and less effective. The use of machine learning algorithms can be a breakthrough in overcoming this problem. Naive Bayes and Classification and Regression Tree (CART) are machine learning algorithms commonly used in the classification process. To improve classification accuracy, these two basic models are integrated with the Discrete Adaptive Boosting (Discrete AdaBoost) algorithm. This study aims to assess the effectiveness of machine learning algorithms in identifying the characteristics of pistachios. Algorithm testing was carried out using the k-fold cross-validation technique. The estimated average performance results of all classification models do not show significant differences. The Discrete AdaBoost CART model has the best accuracy, specificity, and f1-score, at 86.49%, 85.78%, and 88.32%, respectively. Therefore, the Discrete AdaBoost CART model is a suitable model for classifying pistachio types. This shows that ensemble approaches such as Discrete AdaBoost CART can make a significant contribution to improving the performance of classification systems, especially in the context of data with many relevant features. This study was limited to identifying binary classes of pistachios. In further research, it is recommended to explore machine learning algorithms for multiclass of pistachio nuts.
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