Penerapan metode SVM dengan fitur HSV dan HOG pada klasifikasi jenis tanaman hias Aglaonema

Leni Kusneti; Sri Andayani

doi:10.24246/aiti.v23i3.482-495

Authors

Leni Kusneti Universitas Katolik Musi Charitas https://orcid.org/0009-0008-9895-879X
Sri Andayani Universitas Katolik Musi Charitas https://orcid.org/0009-0000-3296-5550

DOI:

https://doi.org/10.24246/aiti.v23i3.482-495

Keywords:

Aglaonema, classification, Support Vector Machine (SVM), HSV, HOG

Abstract

Aglaonema is one of Indonesia's most popular ornamental plants, known for its diverse leaf shapes, colors, and patterns. The visual similarities between varieties often make manual identification challenging. This study aims to classify Aglaonema varieties using the Support Vector Machine (SVM) method with HSV color segmentation and HOG feature extraction. The dataset consists of 1,400 leaf images categorized into seven Aglaonema varieties: Big Roy, Cochin, Dud Anjamani, Heng Heng, Snow White, Lipstick Red, and Butterfly. The results of testing the SVM method with a linear kernel using 5-fold cross-validation showed the highest accuracy for Dud Anjamani at 97.21% and the lowest for Cochin at 92.64%. The highest average precision was achieved by Butterfly at 95.79%, while Cochin had the lowest at 67.99%. The highest average recall was recorded for Big Roy at 91.94%, whereas Butterfly and Lipstick Red had the lowest at 79.00%. Overall, the model achieved an average accuracy of 95.47%, a precision of 85.51%, and a recall of 84.21%.

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