Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method

  • Yudha Islami Sulistya Telkom University
  • Maie Istighosah Telkom University
  • Maryona Septiara Telkom University
  • Abednego Dwi Septiadi Telkom University
  • Arif Amrullah Telkom University

Keywords: Feature Extraction, Kernel Comparison, Machine Learning, Noni Ripeness, SVM

Abstract

The classification of Noni fruit (Morinda citrifolia) ripeness is essential for maximizing its medicinal benefits and ensuring product quality. This research aimed to classify Noni fruit ripeness using the Support Vector Machine (SVM) method, comparing three kernel functions: linear, Radial Basis Function (RBF), and polynomial. A dataset consisting of images of ripe and unripe Noni fruits was utilized, with preprocessing steps including the extraction of color and texture features. Performance evaluation revealed that the RBF kernel achieved the highest accuracy at 86.18%, followed by the polynomial kernel with 84.55%, and the linear kernel with 81.30%. These results suggest that the RBF kernel is the most effective for this classification task, showing superior capability in capturing non-linear patterns and complexities within the dataset.

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Published
2024-12-31
How to Cite
Yudha Islami Sulistya, Istighosah, M., Septiara, M., Septiadi, A. D., & Amrullah, A. (2024). Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method. Indonesian Journal of Data and Science, 5(3), 206-215. https://doi.org/10.56705/ijodas.v5i3.180