CLASSIFICATION OF DRIED MORINGA LEAF QUALITY USING THE NAIVE BAYES METHOD - Pratama, Gede; Sugiartawan , Putu; Kompiang Martina Dinata Putri (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This study aims to classify the quality of dried Moringa leaves (Moringa oleifera) using the Naive Bayes classification method. Moringa leaves are known for their nutritional and medicinal benefits, and their quality significantly affects their value. The classification process focuses on categorizing dried Moringa leaves into different quality grades based on attributes such as color, texture, and overall appearance. Data were collected from samples of dried Moringa leaves, and feature extraction was performed to quantify these attributes. The Naive Bayes classifier, a probabilistic model based on Bayes' Theorem, was used to classify the quality of the samples into predefined categories. The performance of the classifier was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Results indicated that the Naive Bayes method is effective in classifying the quality of dried Moringa leaves, achieving high accuracy and reliability. This research demonstrates the potential of machine learning techniques, particularly Naive Bayes, for automating quality assessment in the agricultural and herbal product industries, which can improve efficiency and standardization in product grading.