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All Journal Indonesian Journal of Data and Science
Amrullah, Arif
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method Yudha Islami Sulistya; Istighosah, Maie; Septiara, Maryona; Septiadi, Abednego Dwi; Amrullah, Arif
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.180

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.
Co-Authors Abednego Dwi Septiadi Maie Istighosah Maryona Septiara Yudha Islami Sulistya