Classification of Palm Fruit Ripeness Level Using Learning Vector Quantization (LVQ) Method

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Published Dec 11, 2023
DOI https://doi.org/10.20895/inista.v6i1.1273

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Deddy Rudhistiar
Department of Informatics Engineering, Institut Teknologi Nasional Malang
Widhy Wahyani
Department of Digital Business, Institut Teknologi Nasional Malang
Thesa Adi Saputra Yusri
Department of Mathematics, Universitas Negeri Yogyakarta
https://orcid.org/0000-0002-4844-2620

Abstract

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.

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How to Cite
Rudhistiar, D., Wahyani, W., & Yusri, T. (2023). Classification of Palm Fruit Ripeness Level Using Learning Vector Quantization (LVQ) Method. Journal of Informatics Information System Software Engineering and Applications (INISTA), 6(1), 31-41. https://doi.org/10.20895/inista.v6i1.1273
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Vol 6 No 1 (2023): November 2023
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Articles
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