<![CDATA[The implementation of Learning Vector Quantization (LVQ) to classify the ripeness of oil palm fruits is investigated in this study. In addition, this study provides a comprehensive set of procedures ranging from data collection and pre-processing to training and testing of the LVQ model, and finally, the proposed method has been validated by testing it on previously unseen data. Three feature extraction methods, specifically Gray-Level Co-occurrence Matrix (GLCM), Hue, Saturation, and Value (HSV), and t-Distributed Stochastic Neighbor Embedding (t-SNE), were assessed for their performance. The results show that the chosen feature extraction method strongly influences the classification performance. The accuracy of the model employing t-SNE features is notably the highest at 50%, indicating its efficacy in identifying the ripeness level of palm fruits by extracting pertinent features. On the other hand, the GLCM feature has a 40% accuracy in the test data, suggesting that although it captures information on texture, it may not comprehensively encapsulate ripeness characteristics. Additionally, the HSV feature achieves an accuracy of 45%, which is less precise than that of t-SNE. To conclude, this study elucidates the appropriateness of various feature extraction techniques in categorizing the degree of ripeness in palm fruits. The t-SNE feature extraction model stands out as the most efficient option, exhibiting greater precision in comparison to other methodologies.]]>
