Implementation of the Support Vector Machine (SVM) Method for Classifying the Maturity Level of Oil Palm Fruit
DOI:
https://doi.org/10.30871/jaic.v9i5.10783Keywords:
Palm Fruit, Ripeness detection, Image Processing, SVM, AccuracyAbstract
This study discusses the classification of palm fruit ripeness levels using the Support Vector Machine (SVM) method. Palm fruit ripeness significantly affects the yield and quality of the oil produced. By utilizing image processing techniques, colour and texture features are extracted from the fruit images to support the classification process. The SVM model was trained with a dataset covering various ripeness levels, including unripe, ripe, overripe, and rotten. The evaluation results show the high accuracy of the SVM model in identifying ripeness levels. This study highlights the potential of machine learning technology in improving the productivity and quality of agricultural products. Support Vector Machine (SVM) is a machine learning method used to classify data into categories by finding the optimal dividing line between two classes, thereby maximizing the distance between the data from the two classes. SVM itself has proven to be very effective in detecting images, as evidenced by several studies such as detecting the ripeness level of melon fruit, each producing a model with an accuracy level above 86%. Thus, this study uses SVM suitable for use in detecting the ripeness level of oil palm fruit. This study produced an SVM model with an accuracy level of 93%.
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