A Hybrid Model for Palm Sugar Type Classification: Advancing Image-Based Analysis for Industry Applications

Abstract

The accurate classification of palm sugar varieties is essential for maintaining product quality and ensuring consumer satisfaction within the food industry. This study proposes a hybrid model that integrates feature Hue, Saturation, and Lightness (HSL) colour space, feature Gray-Level Co-occurrence-Matrix (GLCM) texture analysis, and Fuzzy K-Nearest Neighbors (FKNN) algorithms to classify different categories of palm-sugar, including aren texture, coconut texture, and lontar texture varieties. The hybrid approach leverages advanced image processing techniques to extract and analyze critical texture and colour features, achieving a classification accuracy of 98%. The results underscore the potential of this model to enhance operational efficiency, promote product standardization, and support fair market practices in the palm sugar industry. By providing an automated and accurate classification system, this research contributes to both the economic optimization of production processes and the transparency of product quality in the market. Future research related to palm sugar on real-time deployment of the system in industrial environments is essential, with potential integration into automated sorting and packaging systems to further streamline operations.