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

Yudha Islami Sulistya,

Maie Istighosah,

Maryona Septiara

et al.

Indonesian Journal of Data and Science, Journal Year: 2024, Volume and Issue: 5(3), P. 206 - 215

Published: Dec. 31, 2024

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

Language: Английский

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

Yudha Islami Sulistya,

Maie Istighosah,

Maryona Septiara

et al.

Indonesian Journal of Data and Science, Journal Year: 2024, Volume and Issue: 5(3), P. 206 - 215

Published: Dec. 31, 2024

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

Language: Английский

Citations

0