Klasifikasi Kematangan Buah Pepaya Menggunakan Algoritma Support Vector Machine - Zaqila Amanda; Imam Muslem; Fitri Rizani (Fakultas Ilmu Komputer Universitas Almuslim, 2026)
Manually determining papaya ripeness is often inaccurate and subjective. Therefore, a Support Vector Machine (SVM) algorithm is needed to improve the accuracy of papaya ripeness classification. The problem studied is how to apply SVM to accurately classify papaya ripeness. The research methodology includes papaya image capture, image preprocessing, color feature extraction, and classification using SVM. This study focused on three ripeness categories: unripe, semi-ripe, and ripe. The results showed that the SVM method was able to classify unripe papaya with 67% accuracy, semi-ripe papaya with 22% accuracy, and ripe papaya with 70%. The conclusion of this study is that SVM is quite effective in processing color information for papaya ripeness classification and has potential for application in the agricultural industry