Performance Comparison of Boosting Algorithms in Spices Classification Using Histogram of Oriented Gradient Feature Extraction

Abstract

Spice classification is an important task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the HoG feature extraction method and boosting algorithms. The objective of this research is to compare the performance of four different models of boosting algorithms, namely Adaboost Classifier, Gradient Boosting Classifier, XGB Classifier, and Light GBM Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the XGB Classifier model achieved the best performance, with a precision of 0.811, recall of 0.809, and F1-score of 0.809, while the Adaboost Classifier model had the lowest performance, with a precision of 0.709, recall of 0.689, and F1-score of 0.682. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and boosting algorithms. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods.