KLASIFIKASI BUAH JERUK LEMON BERDASARKAN TINGKAT KEMATANGAN MENGGUNAKAN METODE SVM DAN NAIVE BAYES - Mualfah, Desti; Rivaldi, Hardi; Januar Al Amin; Sunanto (Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi, 2025)
This study aims to develop a classification model for determining the ripeness level of lemons (Citrus limon) using digital image analysis. Two methods, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC), were compared to evaluate their performance in terms of accuracy and prediction consistency. The results show that SVM outperformed NBC with an accuracy of 97%, along with precision, recall, and F1-Score of 97% each. The model consistently determined lemon ripeness levels in percentage terms, such as 85% or 95%. In contrast, NBC achieved an accuracy of 82%, with precision, recall, and F1-Score of 83%, 82%, and 83%, respectively. However, NBC was more prone to classification errors, especially in distinguishing between ripe and unripe lemons. In conclusion, the SVM method proved superior to NBC in determining lemon ripeness levels, particularly in handling complex data. SVM's ability to provide accurate and consistent predictions makes it a more effective choice for helping farmers optimize the quality and quantity of lemon production. This study contributes significantly to the application of image processing technology in the agricultural sector.